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we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

Employees of any company need to know whether their employees are happy or sad or they have any problem in their working environment. There should be some mechanism to handle this information about the employee. Employee chatting messages could be analyzed using sentiment analysis and employee mood detection is retrieved based on text analysis. Also, Employee facial expressions can be detected using Image Processing on employee images taken through Web Camera while an employee is chatting with colleagues. Using Image Processing, Emotion of employee such as Happy, Angry, Sad or Normal is detected. Employee analysis report is shown to company management to find whether the employee is satisfied with company or employee is facing some problem in the working environment.

we have proposed a method for real time Hand Gesture Recognition and feature extraction using a web camera. In this approach, the image is captured through webcam attached to the system. First the input image is preprocessed and threshold is used to remove noise from image and smoothen the image. After this apply region filling to fill holes in the gesture or the object of interest. This helps in improving the classification and recognition step. Then select the biggest blob (biggest binary linked object) in the image and remove all small object, this is done to remove extra unwanted objects or noise from image. When the preprocessing is complete the image is passed on to feature extraction phase.The test image is classified in nearest neighbor’s class in training set. The classification results are displayed to user and through the windows text to speech API gesture is translated into speech as well. The training data set of images that is used has 5 gestures, each with 50 variations of a single gesture with different lighting conditions. The purpose of this is to improve the accuracy of classification.

Purpose: Multifocal ERG is a useful indicator of diabetic retinopathy. The significant delay in local responses provides a chance for the detection and understanding of the various stages of diabetic retinopathy. Materials and Methods: This is a cross sectional study conducted in ERG clinic at M & J Western regional institute of ophthalmology, Ahmedabad from January 2013 to September 2015 who were more than 35yrs of age. Results: In our study, we studied 45 eyes of diabetic patients and 20 eyes of normal subjects. In our study the mean values of the various parameters was calculated in the control group with N1, P1 and N2 latency being 14.09ms. 29,76ms and 45.55ms respectively. The N1, P1 and N2 amplitude was found to be 31.52nV, 73.61nV and 90.38nV respectively. The maximum delay in N1, P1 and N2 latency was seen to be 3.24ms, 7.11ms and 8.40ms respectively from the normal value. We also found a decrease in amplitude of the ERG waveform with N1, P1 and N2 amplitude being 20.98nV, 61.48nV and 76.4nV respectively from the normal value. Also it is helpful in cases with clinically significant macular edema where responses are remarkably delayed suggesting local retinal dysfunction and macular pathology. It provides us information regarding the condition of the macula and some ideas about the extent of ischemia affecting this area. Conclusion: In conclusion, we can say that the delayed responses obtained indicate abnormal retinal function corresponding to local discrete retinal lesions. It provides a very sensitive and objective assessment of the local retinal condition in various stages of diabetic retinopathy. KEYWORDS: Diabetes mellitus; Diabetic retinopathy; Multifocal electroretinogram.

Purpose: Multifocal ERG is a useful indicator of diabetic retinopathy. The significant delay in local responses provides a chance for the detection and understanding of the various stages of diabetic retinopathy. Materials and Methods: This is a cross sectional study conducted in ERG clinic at M & J Western regional institute of ophthalmology, Ahmedabad from January 2013 to September 2015 who were more than 35yrs of age. Results: In our study, we studied 45 eyes of diabetic patients and 20 eyes of normal subjects. In our study the mean values of the various parameters was calculated in the control group with N1, P1 and N2 latency being 14.09ms. 29,76ms and 45.55ms respectively. The N1, P1 and N2 amplitude was found to be 31.52nV, 73.61nV and 90.38nV respectively. The maximum delay in N1, P1 and N2 latency was seen to be 3.24ms, 7.11ms and 8.40ms respectively from the normal value. We also found a decrease in amplitude of the ERG waveform with N1, P1 and N2 amplitude being 20.98nV, 61.48nV and 76.4nV respectively from the normal value. Also it is helpful in cases with clinically significant macular edema where responses are remarkably delayed suggesting local retinal dysfunction and macular pathology. It provides us information regarding the condition of the macula and some ideas about the extent of ischemia affecting this area. Conclusion: In conclusion, we can say that the delayed responses obtained indicate abnormal retinal function corresponding to local discrete retinal lesions. It provides a very sensitive and objective assessment of the local retinal condition in various stages of diabetic retinopathy. KEYWORDS: Diabetes mellitus; Diabetic retinopathy; Multifocal electroretinogram.

Purpose: Multifocal ERG is a useful indicator of diabetic retinopathy. The significant delay in local responses provides a chance for the detection and understanding of the various stages of diabetic retinopathy. Materials and Methods: This is a cross sectional study conducted in ERG clinic at M & J Western regional institute of ophthalmology, Ahmedabad from January 2013 to September 2015 who were more than 35yrs of age. Results: In our study, we studied 45 eyes of diabetic patients and 20 eyes of normal subjects. In our study the mean values of the various parameters was calculated in the control group with N1, P1 and N2 latency being 14.09ms. 29,76ms and 45.55ms respectively. The N1, P1 and N2 amplitude was found to be 31.52nV, 73.61nV and 90.38nV respectively. The maximum delay in N1, P1 and N2 latency was seen to be 3.24ms, 7.11ms and 8.40ms respectively from the normal value. We also found a decrease in amplitude of the ERG waveform with N1, P1 and N2 amplitude being 20.98nV, 61.48nV and 76.4nV respectively from the normal value. Also it is helpful in cases with clinically significant macular edema where responses are remarkably delayed suggesting local retinal dysfunction and macular pathology. It provides us information regarding the condition of the macula and some ideas about the extent of ischemia affecting this area. Conclusion: In conclusion, we can say that the delayed responses obtained indicate abnormal retinal function corresponding to local discrete retinal lesions. It provides a very sensitive and objective assessment of the local retinal condition in various stages of diabetic retinopathy. KEYWORDS: Diabetes mellitus; Diabetic retinopathy; Multifocal electroretinogram.

Purpose: Multifocal ERG is a useful indicator of diabetic retinopathy. The significant delay in local responses provides a chance for the detection and understanding of the various stages of diabetic retinopathy. Materials and Methods: This is a cross sectional study conducted in ERG clinic at M & J Western regional institute of ophthalmology, Ahmedabad from January 2013 to September 2015 who were more than 35yrs of age. Results: In our study, we studied 45 eyes of diabetic patients and 20 eyes of normal subjects. In our study the mean values of the various parameters was calculated in the control group with N1, P1 and N2 latency being 14.09ms. 29,76ms and 45.55ms respectively. The N1, P1 and N2 amplitude was found to be 31.52nV, 73.61nV and 90.38nV respectively. The maximum delay in N1, P1 and N2 latency was seen to be 3.24ms, 7.11ms and 8.40ms respectively from the normal value. We also found a decrease in amplitude of the ERG waveform with N1, P1 and N2 amplitude being 20.98nV, 61.48nV and 76.4nV respectively from the normal value. Also it is helpful in cases with clinically significant macular edema where responses are remarkably delayed suggesting local retinal dysfunction and macular pathology. It provides us information regarding the condition of the macula and some ideas about the extent of ischemia affecting this area. Conclusion: In conclusion, we can say that the delayed responses obtained indicate abnormal retinal function corresponding to local discrete retinal lesions. It provides a very sensitive and objective assessment of the local retinal condition in various stages of diabetic retinopathy. KEYWORDS: Diabetes mellitus; Diabetic retinopathy; Multifocal electroretinogram.