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Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus...
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a novel method for rotation invariant insulator detection. Rotation invariance is achieved by an efficient approach for estimating rotation angle of all insulator of an image. Sliding window based local directional pattern (LDP) feature is extracted from the image and support vector...
This paper proposes a novel approach for human activity recognition based on body part histograms and Hidden Markov Models. From a depth video frame, body parts are segmented first using a trained random forest. Then, a histogram for each body part is combined to represent histogram features for a depth image. The depth video activity features are then applied on hidden Markov models for training...
This paper presents an efficient image exploration scheme for the unshaped object using semantic modelling. The local regions of an image have been classified with respect to the frequency of occurrences. The semantic concept is evaluated using RGB histogram dissimilarity factor, overall dissimilarity factor and regional dissimilarity factor. The dissimilarities determine the local concept with accuracy...
An efficient algorithm is proposed that enhances the traditional mean-shift color histogram based object tracking approaches. We propose prominent local directional pattern variance (LDPT) that can extract directional texture responses and with color we develop a color-texture histogram representation for the target. Experiments show superior performance that allows target to go under complex environment...
This paper presents a moving-object segmentation algorithm using texture information along the edge segment. The proposed method is developed to address challenges due to variations in ambient lighting and background contents. We investigated the suitability of the proposed algorithm in comparison with the traditional edge-pixel-based and edge-segment-based detection methods. In our method, edges...
This paper presents a novel local feature descriptor, the Local Directional Pattern (LDP), for describing local image feature. A LDP feature is obtained by computing the edge response values in all eight directions at each pixel position and generating a code from the relative strength magnitude. Each bit of code sequence is determined by considering a local neighborhood hence becomes robust in noisy...
Automatic facial expression recognition is a challenging problem in computer vision, and has gained significant importance in applications of human-computer interaction. This paper presents a new appearance-based feature descriptor, the Local Directional Pattern Variance (LDPv), to represent facial components for human expression recognition. In contrast with LDP, the proposed LDPv introduces the...
In this paper, we present a novel texture descriptor Local Directional Pattern (LDP) to represent facial image for gender classification. The face area is divided into small regions, from which LDP histograms are extracted and concatenated into a single vector to efficiently represent the face image. The classification is performed by using support vector machines (SVMs), which had been shown to be...
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