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Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

Presurgical discrimination of 1p/19q codeletion status may have prognostic and diagnostic value for glioma patients for immediate personalized treatment. Artificial intelligence-based models have been proved as effective method to demonstrate computer aided diagnostic system for glioma cancer. An objective of study is to present an advanced biomedical texture descriptor to perform machine learning-assisted identification of 1p/19q codeletion status of low-grade glioma (LGG) cancer. An aim is to verify efficacy of textures, extracted using local binary pattern and derived from gray level co-occurrence matrix (GLCM). Proposed study used random forest-assisted radiomics model to analyse MRI images of 159 subjects. Four different advanced biomedical texture descriptors are proposed by experimenting different extensions of LBP method. These variants-(as variant I to IV) with 8-bit or 16-bit or 24-bit LBP codes are applied with different orientations in 5 × 5, 7 × 7 square-sized neighbourhood, which are recorded in LBP histograms. These histogram features are concatenated by GLCM-based textures including energy, correlation, contrast and homogeneity. Texture descriptors performed best with classification accuracy of 87.50% (AUC: 0.917, sensitivity: 95%, specificity: 75%, f1-score: 90.48%) using 8-bit LBP variant-I. 10-fold cross-validated accuracy of all four sets range from 65.62% to 87.50% using random forest classifier and mean-AUC range from 0.646 to 0.917.

Handwritten digit recognition is an essential step in understanding handwritten documents. The state-of-the-art convolutional neural networks (CNN) methods are mostly preferred for recognizing handwritten digits. Though the recognition accuracy is high, CNN filter weights don’t change even after training. Hence the process cannot adjust dynamically to changes in input. Recently the interest of researchers of computer vision has been on Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The use of transformer architecture enabled substantial parallelization and translation quality improvement. The inadequacies of CNNs sparked a hybrid model revolution, which combines the best of both disciplines. This paper is written to view the impact of the hybrid model on handwritten digit recognition. The testing is done on the available benchmark datasets, the Extended Modified National institute of standards and technology (EMNIST) digits dataset, and the most significant historical handwritten digit dataset (DIDA). The 10-fold cross-validation accuracy achieved on EMNIST and DIDA is 99.89% and 99.73%, respectively. The results show that the proposed method achieves the highest accuracy compared to existing methodologies. The proposed method is robust, feasible, and effective on clean and uncleaned images.

With newly emerging technologies in the field of computer science, there is rising awareness about its applications in the medical sciences. One of such important applications is early and accurate diagnosis of cancer tumor. Brain tumor is a deadly disease and needs to be diagnosed accurately on time. Among many types of brain tumors, high-grade glioma (HGG) is the most belligerent type, and the aggressive nature of tumors affects the survival outcomes of patients. Image processing and deep learning techniques have helped a lot in this endeavor. The proposed work deals with a hybrid methodology that combines simple edge detection technique with deep convolutional neural network to achieve state-of-the-art results. The Brain Tumor Segmentation (BRATS) 2018 dataset is used, which is provided under BRATS contest organized by MICCAI international conference (Medical Image Computing and Computer-Assisted Intervention). The Sobel operator is used for edge detection, and such edge-detected images are further trained using the modified deep UNET (md-UNET)-based model to segment glioma tumor regions into three classes namely—enhancing tumor, non-enhancing necrotic tumor, and edema. The data of 210 patients is used for training the proposed model using train-validate-test split. The model has achieved an accuracy of 98.94%, dice score of 99.03%, and dice loss of 0.0096 for separate test cohort of 6720 multi-modal MRI images of 40 glioma patients.

Nowadays, machine learning-based quantified assessment of glioma has recently gained more attention by researchers in the field of medical image analysis. Such analysis makes use of either hand-crafted radiographic features with radiomic-based methods or auto-extracted features using deep learning-based methods. Radiomic-based methods cover a wide spectrum of radiographic features including texture, shape, volume, intensity, histogram, etc. The objective of the paper is to demonstrate the discriminative role of textures for molecular categorization of glioma using supervised machine learning techniques. This work aims to make state-of-the-art machine learning solutions available for magnetic resonance imaging (MRI)-based genomic analysis of glioma as a simple and sufficient technique based on single feature type, i.e., textures. The potential of this work demonstrates importance of texture features using simple, computationally efficient local binary pattern (LBP) method for isocitrate dehydrogenase (IDH)-based discrimination of glioma as IDH mutant and IDH wild type. Further, such texture-based discriminative analysis alone can definitely facilitate an immediate recommendation for further diagnostic decisions and personalized treatment plans for glioma patients.

Recent developments in glioma categorization based on biological genotypes and application of computational machine learning or deep learning based predictive models using multi-modal MRI biomarkers to assess these genotypes provides potential assurance for optimal and personalized treatment plans and efficacy. Artificial intelligence based quantified assessment of glioma using MRI derived hand-crafted or auto-extracted features have become crucial as genomic alterations can be associated with MRI based phenotypes. This survey integrates all the recent work carried out in state-of-the-art radiomics, and Artificial Intelligence based learning solutions related to molecular diagnosis, prognosis, and treatment monitoring with the aim to create a structured resource on radiogenomic analysis of glioma. Challenges such as inter-scanner variability, requirement of benchmark datasets, prospective validations for clinical applicability are discussed with further scope for designing optimal solutions for glioma stratification with immediate recommendations for further diagnostic decisions and personalized treatment plans for glioma patients.

Recently, image based plant phenotyping is used extensively for plant trait estimation, plant accession classification and selection, and plant stress analysis. Plant accession classification and selection helps in identification of plants tolerant to local climatic conditions. Over the past few years, convolutional neural network (CNN) models were predominantly used for classification of plants and plant diseases from static plant images using spatial features. In this paper, we introduce three different methods namely 3-dimensional (3-D) CNN, CNN with convolutional long short-term memory (ConvLSTM) layers and vision transformer that use temporal information along with spatial information for plant accession classification. We have used publicly available Arabidopsis plant accession classification dataset to test the performance of these methods. Time series color images of four different plant accessions of Arabidopsis are given as input to the neural network model which in turn identifies plant accession. All the three methods outperform the existing methods available in the literature in terms of average accession classification accuracy. Vision transformer achieves highest classification accuracy of 98.59% at the cost of very large number of trainable parameters as compared to the other two methods. On the other hand, CNN-ConvLSTM achieves comparable accuracy of 97.97% with very less trainable parameters as compared to vision transformer. In future, these models can also be used to identify unknown plant accessions and predict plant growth signatures in different climatic conditions.

Recent advancements in the area of computer vision-based plant phenotyping are playing an important role, in determining the quantitative phenotypes of plants, and crop yield. Automatic segmentation of plants and its associated structures is the first and most important step in image based plant phenotyping. We design and implement convolutional neural network (CNN) based modified residual U-Net for semantic segmentation of plants from the background. We also use SegNet and U-Net architectures for comparison purpose. In this paper, residual U-Net, SegNet and U-Net models are tested on leaf segmentation challenge (LSC) dataset and fig dataset that are publicly available. LSC dataset consists of images of Arabidopsis and tobacco plants grown under controlled conditions whereas fig dataset includes top view images of fig plants captured in open-field conditions. We have used 8 evaluation metrics for analyzing and comparing the performance of residual U-Net, SegNet and U-Net architectures with the existing algorithms in the literature. Residual U-Net with 15.32 million trainable parameters, outperforms SegNet and other state-of-the-art methods whereas achieves comparable performance with respect to U-Net. Residual U-Net achieves dice coefficient of 0.9709 on LSC dataset and 0.9665 on fig dataset, respectively. The segmentation networks used in this paper can be used for other plant related applications such as plant trait estimation or in quantification of plant stress.

In the last few decades, content-based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content-based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content-based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorised as: 1) image annotation techniques to define the high-level concepts in image; 2) relevance feedback techniques to integrate user's perception; 3) machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic-based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content-based image retrieval. The paper concludes with promising future research directions.

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.

Classifying gliomas noninvasively into their molecular subsets is a crucial neuro-scientific problem. Prognosis of the isocitrate dehydrogenase (IDH) mutation in gliomas is important for planning targeted therauptic intervention and tailored treatment for individual patients. This work proposes a novel technique based on texture analysis of T2-weighted magnetic resonance imaging (MRI) scans of grade 2 and grade 3 gliomas to differentiate between IDH1 mutant 1p/19q positive and IDH1 mutant 1p/19q negative categories. The textural features used in the proposed method are local binary patterns histogram (LBPH), Shannon entropy, histogram, skewness, kurtosis, and intensity grading. We discriminate the tumors into their molecular subtypes using standard artificial neural networks (ANNs). LBPH attributes demonstrated maximum discrimination between the two groups followed by Shannon entropy. In summary, the technique proposed facilitates an early biomarker to detect the IDH subtype noninvasively and can be employed as an automated tool in clinics to aid diagnosis.

Human age classification via face images is not only difficult for human being but also challenging for a machine. But, because of potential applications in the field of computer vision, this topic has attracted attention of many researchers. In this paper, a novel two stage age classification framework based on appearance and facial skin ageing features with multi-class support vector machine (M-SVM) is proposed to classify the face images into seven age groups. Appearance features consist of shape features such as, geometric ratios and face angle and facial skin textural features extracted by using local Gabor binary pattern histogram (LGBPH). Facial skin ageing features consist of facial skin textural features and wrinkle analysis. The proposed age classification framework is trained and tested with face images collected from FG-NET ageing database and PAL face database and achieved greatly improved age classification accuracy of 94.45%.

Human age classification via face images becoming an interesting research area because of potential applications in the field of computer vision such as Age Specific Human Computer Interaction (ASHCI), biometrics, security and surveillance, etc. In this paper, a novel method for human age classification using facial skin analysis for aging feature extraction and Multi-class Support Vector Machine (M-SVM) for age classification is proposed to classify the face images into four age groups. Facial skin analysis consists of skin texture analysis and wrinkle analysis. Gabor wavelet is used to analyze the facial skin textural changes with age progression. Wrinkle analysis detects the wrinkle density changes at particular regions on face image with age progression. The performance evaluation of proposed age classification system is carried out by using face images from PAL face database. In this paper, the performance of M-SVM classifier is compared with the performance of Artificial Neural Network (ANN) classifier for the task of human age classification using Gabor wavelet and wrinkle analysis. The result analysis concludes that the best age classification accuracy of 93.61% is achieved by using proposed age classification system and M-SVM is an efficient classifier than the ANN classifier for the task of human age classification in combination with Gabor wavelet and wrinkle analysis.

The ageing patterns reflected on the human face with age progression can be used directly to develop age-based intelligent systems for the applications such as biometrics, security and surveillance, etc. This paper presents a novel human age classification system based on local binary patterns (LBP) and wrinkle analysis for ageing feature extraction and multi-class support vector machine (M-SVM) for age classification to classify the face images into four age classes. In this paper, the potential of LBP to capture the minute variations in the textures has been exploited to extract an efficient and robust ageing features invariant to illumination and rotation changes. The proposed age classification system is trained and tested with the face images from PAL face database and achieved the improved age classification accuracy of 91.39%. The experimental results comparison of MSVM classifier with nearest neighbour (k-NN) classifier and artificial neural network (ANN) classifier concludes that M-SVM classifier is the best classifier among the three classifiers for the task of human age classification using LBP and wrinkle analysis for ageing feature extraction.

Human face shows different patterns on the face for particular age class. These ageing patterns on the human faces can be directly used for developing age based systems. Computer based age classification via face images become an interesting topic recently because of their real world applications such as electronic customer relationship management, security and surveillance, etc. In this paper, the detailed survey has been made on the techniques and algorithms developed by researchers for age classification via face images. Comparative study has been done in this paper on existing age classification methods on the basis of pre-processing techniques, ageing feature extraction methods, age classifiers, system performances and databases used for evaluation of system. Future research directions related to the topic of automatic human age classification via face images are also mentioned in this paper.

This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and artificial neural network (ANN) as age classifier. The appearance features consist of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local binary pattern histogram (LBPH) of regions of interest in face images are used as textural features. The ANN is designed by using two layer feedforward backpropagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female, respectively.