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Stereo matching is a fundamental task in vision applications. we propose an adaptive cross-scale aggregation method for stereo matching, which is introduced by solving an optimization problem. Unlike the original approach which introduces the same regularization term based on the inter-scale regularizer parameter to control the cost consistency among the multi-scales for all regions of the input images...
In this study, we propose a novel shape-based traffic sign detection method which consists of two stages. First, a rotational symmetry voting scheme is proposed to detect the centers and boundary sets of the candidate polygons in the image. Second, a Link Distribution (LD) model, which considers a polygon as the collection of links between center and boundary points, is proposed to refine the detection...
Inappropriate medication use such as wrong drug or wrong dose intake can be harmful to patients. In this work we present a method to automatically identify a pill from a single image using Convolutional Neural Network (CNN). We first localize the pill in the image by detecting the region with the highest concentration of edges. To overcome the challenge of minimal labeled training data and domain...
Pill identification is a serious concern for pharmacists due to similarity of pill appearances. Pill imprints usually contain important information that can be used to add or search for pill information on existing pill databases. However, current techniques for extracting imprints often give results as vectors which cannot be used with existing databases. Thus, this paper proposed an approach for...
Plants are to be considered as one of the important things that plays a very essential role for all living beings exists on earth. But due to some unawareness and environment deterioration, some very rare plants are on the verge of extinction. Knowledge of rare leaves used for medicine and other plants is very critical in future. Leaf identification and classification plays a vital role for plant...
Given the challenges of manual operation, there is an impending need for automation of the baggage handling problem. Imparting sensory capabilities is important in the creation of intelligent robotic systems that can achieve this task. In this paper, we focus on vision as it is one of the dominant perceptual modalities. Specifically, we propose a part based edge alignment technique based on Chamfer...
Sketch-based image retrieval (SBIR) is a challenging task due to the ambiguity inherent in sketches when compared with photos. In this paper, we propose a novel convolutional neural network based on Siamese network for SBIR. The main idea is to pull output feature vectors closer for input sketch-image pairs that are labeled as similar, and push them away if irrelevant. This is achieved by jointly...
ShapeNets is an image representation, which is based on shape, compact structure, hierarchical image structure and appearance characteristic of object contour. In a ShapeNets, the shape of image is a window of containing objects which can be extracted with the method of objectness. The outline of objects can also be extracted in a line boundary detection algorithm based on histogram of gradients direction,...
The number of known and unknown plant species increases as time goes by. Research on plant species can be further advanced if there is a quick and accurate system that can identify plants and hasten the classification process. This system will not only help in accelerating plant classification, but will also allow people who are not morphological experts to conduct their own studies. LeaVes is an...
A method for automatic detection or recognition using high-frequency forward-looking imaging sonar is proposed. The method includes segmentation of an underwater object's echo shape or its acoustic shadow shape. For accuracy of segmentation, some constraints were assumed on the underwater ground and the sonar's pose. The separated shape was compared to simulated reference shapes to know its orientation...
In this work, the merits of class-dependent image feature selection for real-world material classification is investigated. Current state-of-the-art approaches to material classification attempt to discriminate materials based on their surface properties by using a rich set of heterogeneous local features. The primary foundation of these approaches is the hypothesis that materials can be optimally...
Archival of images in databases, enabling further study with respect to their contents, is at our focus of attention. The major difficulties are i) the processing of a large number of images, ii) that the steadily growing number of images increase the complexity of the pattern recognition problems to be solved. We propose orientation radiograms, to be used as image signatures for shape based queries...
Achieving sub-pixel accuracy with face alignment algorithms is a difficult task given the diversity of appearance in real world facial profiles. To capture variations in perspective, occlusion, and illumination with adequate precision, current face alignment approaches rely on detecting facial landmarks and iteratively adjusting deformable models that encode prior knowledge of facial structure. However,...
This paper proposes extended Generalized Hough Transform (GHT) to introduce training process by using Partial Least Squares (PLS) regression analysis. Hough transform can robustly detect patterns against noise and occlusions, and GHT is adapted to perform the generic object detection. In this study, we introduced training process to determine the voting weight of GHT by using PLS regression analysis...
In this paper, we propose a method for spotting keywords in images of handwritten text. Relying on an object detection system in real images, local contour features are extracted from segmented word images in order to obtain a representative shape of a word-class. Thus, word spotting is cast following a query-by-word-class scenario where class models are generated using a random subset of the images...
Contour detection is an important and challenging task in computer vision, with many applications in the analysis of natural scenes and biomedical images. Although there are many general approaches to contour detection, achieving good performance in any given application often requires considerable hand-tuning of algorithm parameters, optimization criteria, or pre-processing of the images themselves...
Colonoscopy is the primary method for detecting and removing polyps — precursors to colon cancer, but during colonoscopy, a significant number of polyps are missed — the pooled miss-rate for all polyps is 22% (95% CI, 19%–26%). This paper presents an automatic polyp detection system for colonoscopy, aiming to alert colonoscopists to possible polyps during the procedures. Given an input image, our...
Invariant descriptor for shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary Pattern (LBP) in order to encode the information distinguishing them. Local Binary Pattern (LBP) ensures encoding global and...
In the real-world unconstrained face recognition scenarios, automatic facial landmarking scheme using the active shape model (ASM) usually gives non-ideal results, especially at the facial boundary. This is because the local subspace methods such as the principal component analysis (PCA) used in the ASM does not properly discern skin texture and background with very similar photometric and textual...
For background-subtraction-based moving object detection, reliable background modeling is the most important component. Pixel-based methods are sensitive to illumination change, and edge-based methods can solve illumination-related problems, but have shape distortion problems. In this paper, we propose an edge-segment-based statistical background modeling algorithm and an online update mechanism to...
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