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Copy-move forgery is one of the most tampered means. In this paper, we propose a blind method based on the color label and oriented color texture feature for copy-move detection. Firstly, we compute local color entropy of every pixel, which is grouped into several categories as color labels. Then an image is divided into overlapping blocks, oriented color texture feature of which is extracted. Similar blocks are searched in these blocks with the same color label, and then we fuse these similar block pairs into several regions. According to the linkage relation of these regions, the tampered regions are located. Experiment results have demonstrated that the proposed algorithm has good performance in terms of improved detection accuracy and reduced execution time, at the same time, it also can detect these tampered images inpainted by exemplar-based inpainting technique.

Breast cancer is one of the most common human neoplasms in women, commonly diagnosed through histopathological microscopy imaging. The automated classification of histopathology images can relieve some workload of pathologists by triaging the cases. Knowing that histopathological images show a high degree of variability, useful information is often obtained at different optical magnification levels in order to make the correct diagnosis. For automated scoring, if there are differences in the patient’s score at each considered magnification, the decision may not be reliable if only one magnification level is taken into consideration. This study proposes an integrated model in which scores across magnifications are combined by weights estimated from the least square methods. Moreover, unlike the existing methods, we consider a novel heterogeneous committee which includes deep and traditional members, to design a system for each magnification. As few studies have shown, such in an ensemble, often only a subset of members is sufficient to provide enough discriminative information. Hence, we use an information theoretic measure (ITS) to select optimal members for each magnification. We use publicly available BreaKHis dataset for the experimentation, and demonstrate that the proposed approach yield comparable or better performance when compared with most CNN based frameworks.

Currently, colon cancer diagnosis is based on manual assessment of tissue samples stained with hematoxylin and eosin (H&E). This is a high volume, time consuming, and subjective task which could be aided by automatic cancer detection. We propose an algorithm for automatic cancer detection within WSI H&E stains using a multi class colon tissue classifier based on features extracted from 5 different color representations. Approx. 32000 tissue patches were extracted for the classifier from manual annotations of 9 representative colon tissue types from 74 WSI H&E stains. Colon tissue classifiers based on gray level or color features were trained using leave-one-out forward selection. The best colon tissue classifier was based on color texture features obtaining an average tissue precision-recall (PR) area under the curve (AUC) of 0.886 and a cancer PR-AUC of 0.950 on 20 validation WSI H&E stains.

Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest paths in the HSI space to build a texture representation for classification. In particular, two undirected graphs are used to model the H channel and the S and I channels respectively in order to represent a color texture image. Moreover, the shortest paths is constructed by using four pairs of pixels according to different scales and directions of the texture image. Experimental results on colored Brodatz and USPTex databases reveal that our proposed method is effective, and the highest classification accuracy rate is 96.93\(\%\) in the Brodatz database.

The defects in tiles are directly related with changes in the structure or color components producing spots or stains in the final product. Usually, a visual inspection is carried out in order to detect one of such common defects in tiles; however this process depends on the expertise and abilities of the operator on duty. In this paper, we present the automation of defect detection in tiles using vision algorithms and Artificial Neural Networks (ANN). Color and texture information extracted from real tile images are used as input to a classifier based on neural networks. Setting parameters for extracting the texture attributes are obtained performing detailed tests of different distances, orientations and window sizes. An initial architecture of the ANN is obtained using texture features extracted from Brodatz images. Next, the neural network parameters are computed using real images from the tile database. The experimental tests validate the global performance, accuracy and feasibility of our approach.

In this article we present a novel method of extraction and combination descriptor to represent image. First we extract a descriptor shape (HOG) from entire image, and in second we applied method of segmentation and then we extract the color and texture descriptor from each segment in order to have a local and global aspect for each image. These characteristics will be concatenate, stored and compared to those of the image query using the Euclidean distance. The performance of this system is evaluated with a precision factor. The results experimental show a good performance.

The leaves are an important plant organ and source of information for the traditional plant taxonomy. This study proposes a plant classification approach using the adaxial epidermis tissue, a specific cell layer that covers the leaf. To accomplish this task, we apply a high discriminative color texture analysis method based on the Bouligand-Minkowski fractal dimension. In an experimental comparison, the success rate obtained by our proposed approach (\(96.66\%\)) was the highest among all the methods used, demonstrating that the Bouligand-Minkowski method is very suitable to extract discriminant features from the adaxial epidermis. Thus, this research can significantly contribute with other studies on plant classification by using computer vision.

Changing clothes is a basic activity of daily living (ADL) which may be used as a measurement of the functional status of e.g. an elderly person, or a person with certain disabilities. In this paper we propose a methodology for the detection of when a human has changed clothes. Our non-contact unobtrusive monitoring system is built upon the Microsoft Kinect depth camera. It uses the OpenNI SDK to detect a human skeleton and extract the upper and lower clothes’ visual features. Color, texture and edge descriptors are then extracted and fused. We evaluate our system on a publicly available set of real recordings for several users and under various illumination conditions. Our results show that our system is able to successfully detect when a user changes clothes, thus to assess the quality of the corresponding ADL.