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Medicinal plants are getting increasingly popular across the world for their ability to cure different diseases including chronic ones. The chemical compositions present in those plant leaves are main contributors for the healing characteristics. The potential of using such plants also depends on the maturity of the medicinal plant under use. The leaves with appropriate maturity can cause better healing...
In this paper, we propose an instrumentation and computer vision pipeline that allows automatic object detection on images taken from multiple experimental set ups. We demonstrate the approach by autonomously counting intoxicated flies in the FLORIDA assay. The assay measures the effect of ethanol exposure onto the ability of a vinegar fly Drosophila melanogaster to right itself. The analysis consists...
This paper describes different approaches for detection and identification of diseases in apples using computer vision. Our proposed algorithms analyze surface appearance of apple for defects using image features, viz. color and texture. For segmentation of Region Of Interest (ROI), K-means clustering is performed over the image pixels based on their intensity values. For creation of feature vector,...
Superpixel generation is a common preprocessing step in vision processing aimed at dividing an image into non-overlapping regions. Simple Linear Iterative Clustering (SLIC) is a commonly used superpixel algorithm that offers a good balance between performance and accuracy. However, the algorithm's high computational and memory bandwidth requirements result in performance and energy efficiency that...
Image segmentation is used in computer vision, medical imaging, and biological imaging to locate object boundaries and to group similar pixels together to form a set of coherent image regions. The important factors of clustering are similarity, proximity, and good continuation, which lead to visually meaningful segmentation. On the contrary, there are some problems of visual grouping such as over-segmentation,...
Nowadays the systems able to recognize objects are very important. They become more expected every day. We meet that kind of technologies everywhere. Medicine use them for diagnostic purposes, for remote manipulation with devices in surgery, military services use them for testing and simulation, social services automate process using special industrial robots with computer vision, video surveillance...
In this paper, a novel surveillance video summaryzation approach is proposed to detect the objects and targets which appear less frequently. This approach integrates clustering and background subtraction. The clustering method adopts a modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to summarize the surveillance video with HSV color feature. Then the background...
A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels are perceptually consistent units which carry more information than pixels and adhere well to image boundaries. Nowadays superpixels are widely used for segmentation in computer vision and biomedicai applications. There are many approaches to generate superpixels such as SLIC, QuickShift,...
In this paper, we propose an interactive clothing image segmentation method based on super pixels and Graph Cuts. Firstly, we process the image from pixels to super pixels with the method of SLIC to reduce the computational loads and lower the effect of noise, and then a graph is constructed using super pixels as nodes. Finally, min-cut/max-flow algorithm is applied to solve the energy function. In...
In order to address the problem that the pedestrian segmentation in infrared image is easy to be interfered by the human pose and noise, this paper presents a pedestrian segmentation algorithm in infrared images employing super pixel and conditional random filed. Owing to accelerate the computation, the algorithm employs the simple linear iterative clustering algorithm to divide the image into some...
In this paper we introduce the Reduction Sweep algorithm, a novel graph-based image segmentation algorithm that is designed for easy parallelization. It is based on a clustering approach focusing on local image characteristics. Each pixel is compared with its neighbors in an implicitly independent manner, and those deemed sufficiently similar according to a color criterion are joined. We achieve fast...
In this paper, we proposed a super pixel based depth map propagation algorithm for the application in 2D to 3D video conversion. The proposed algorithm employs four main processes to generate depth maps for all frames in video sequences. First, the depth map of the key frames in the input sequences are generated by manual work. Second, the frames in the input sequences are over-segmented by Simple...
Image segmentation is a hard task and many methods have been developed to alleviate its difficulties. A common preprocessing step designed for this purpose is to compute an over-segmentation of the image, often referred to as superpixels. In this paper, we propose a new approach to superpixels computation. In a first step, a hypergraph-based representation of the image is built. Then, a coarsening...
This study introduces a novel classification algorithm for learning and matching sequences in view independent object tracking. The proposed learning method uses adaptive boosting and classification trees on a wide collection (shape, pose, color, texture, etc.) of image features that constitute a model for tracked objects. The temporal dimension is taken into account by using k-mean clusters of sequence...
Image segmentation is a fundamental task in many computer vision applications. In this paper, we describe a new unsupervised color image segmentation algorithm, which exploits the color characteristics of the image. The introduced system is based on a color quantization of the image in the Lab color space using the popular eleven culture colors in order to avoid the well known problem of oversegmentation...
We consider pixel labeling problems where the label set forms a tree, and where the observations are also labels. Such problems arise in feature-space analysis with a very large label set, for instance in color image segmentation. In this case a tree of labels can be constructed via hierarchical clustering of the observations. This leads to an obvious distance function between two labels, namely their...
Dams are very important economical and social structures that have a great impact on the population living in surrounding area. Dam surveillance is a complex process which involves data acquisition and analysis techniques, implying both measurements from sensors and transducers placed in the dam body and its surroundings, and also visual inspection. In order to enhance the visual inspection process...
Segmentation, or partitioning images into internally homogeneous regions, is an important first step in many computer vision tasks. In this paper, we attack the segmentation problem using an ensemble of low cost image segmentations. These segmentations are reconciled by applying recent techniques from the consensus clustering literature which exploit a non-negative matrix factorization (NMF) framework...
Pearl's color is an important feature to assess its value, including the hue and its color depth. A method for pearl color classification was investigated in this paper. Computer Vision is used to process the pearl image after transforming it from RGB to HSV color model, which can show the hue and color depth information of pearl. According to the histogram of V (Value) weight, the bright area is...
As computer vision algorithms move to embedded platforms within distributed smart camera systems, greater attention must be placed on the efficient use of storage and computational resources. Significant savings can be made in background modeling by identifying large areas that are homogenous in color and sparse in activity. This paper presents a pixel-based background model that identifies such areas,...
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