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This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering. Unlike most other deep learning strategies applied in this context, our approach tackles these challenging problems by estimating edges and reconstructing images using only cascaded convolutional layers arranged such that...
Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propose two approaches to alleviate the precise vehicle search problem by exploiting...
We introduce a novel method for representation learning that uses an artificial supervision signal based on counting visual primitives. This supervision signal is obtained from an equivariance relation, which does not require any manual annotation. We relate transformations of images to transformations of the representations. More specifically, we look for the representation that satisfies such relation...
Although shadows in images have a constructive role providing a natural view of features of the scene, they also have a destructive role in image processing by hiding significant information. Improving the quality of 3D textured models for serious games and augmented reality applications via shadow detection and removal remains challenging due to the complexity of an image scene. This paper proposes...
Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusion or even privacy...
The aim of this paper is to compare two different types of filter for the diving video processing. The two filters are a boolean filter and a fuzzy filter. These filters are applied for improving the diving video analysis aimed to introduce quantitative tools and diving performance measurement and therefore to improve training. The aim of the filter is to identify the athlete in the video to further...
Usually the static or dynamic characteristics of the flame are extracted for flame detection. But the relationship between the various features of flame could not be distinguished by the human eye. the Gradient Boost Decision Tree (GBDT) is thus proposed to combine and optimize the flame shape and texture features, so as to mine the relationship of flame features. Then the more discriminant new flame...
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...
Security is obligatory for digital world. It requires robust and reliable security mechanisms which comprises irreplaceable identification of individual. Biometrics plays an important role in recognizing individual uniquely, furthermore iris based security is more impenetrable as compared to fingerprint based security. Also, human iris doesn't change with ageing and can be easily captured. Generic...
We present a conditional generative method that maps low-dimensional embeddings of image and natural language to a common latent space hence extracting semantic relationships between them. The embedding specific to a modality is first extracted and subsequently a constrained optimization procedure is performed to project the two embedding spaces to a common manifold. Based on this, we present a method...
Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic...
In this paper, we present a three-stage method for the estimation of the color of the illuminant in RAW images. The first stage uses a convolutional neural network that has been specially designed to produce multiple local estimates of the illuminant. The second stage, given the local estimates, determines the number of illuminants in the scene. Finally, local illuminant estimates are refined by non-linear...
Traffic sign recognition is an important step for integrating smart vehicles into existing road transportation systems. In this paper, an NVIDIA Jetson TX1-based traffic sign recognition system is introduced for driver assistance applications. The system incorporates two major operations, traffic sign detection and recognition. Image color and shape based detection is used to locate potential signs...
From the empirical studies, it is quite difficult for the license plate recognition to perform 100% accuracy in a real world environment. Nevertheless, it is common that only a few characters are misread from a license plate recognition system. In this paper, license plate matching is used for vehicle re-identification. We evaluate several approximate string matching techniques to determine an applicable...
Monitoring phenology of agricultural plants is a critical understanding in precision agriculture. Vital improvements can be achieved with precise detection of phenological change of plants which would henceforth improve the timing for the harvest, pest control, yield prediction, farm monitoring, disaster warning etc. Many countries across the world have been developing initiatives to build national...
Multimedia semantic concept detection is one of the major research topics in multimedia data analysis in recent years. Disaster information management needs the assistance of multimedia data analysis to better utilize those disasterrelated information, which has been widely shared by people through the Internet. In this paper, a Feature Affinity based Multiple Correspondence Analysis and Decision...
In this paper, we propose a new person re-identification algorithm based on bi-directional superpixel earth mover's distance (BD-SP-EMD). To address the viewpoint change issue, the human body segmentation is first extracted based on background modeling and saliency maps. A bi-directional scheme is then applied to obtain the forward and backward SP-EMD distances. Based on these two distances, pedestrians...
In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance...
Color is one of the attributes that play a role in identifying specific objects, color processing including the extraction of information about the spectral properties of the object's surface and look for the best similarity of a set of descriptions which have been known to do an introduction. Therefore, the classification is needed right fuji apples to obtain good quality fruit. Fuzzy model is one...
We propose a real-time algorithm for the generic classification of humans and objects in 3D scenes. The algorithm does not depend on color information and works with depth data alone, making it very flexible for a wide area of applications. Further, we will show that it is very resistant to occlusion and will give correct classification results even in cases, where only a fraction of a full human...
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