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In 3D object recognition, local feature-based recognition is known to be robust against occlusion and clutter. Local feature estimation requires feature correspondences, including feature extraction and matching. Feature extraction is normally a two-stage process that estimates keypoints and keypoint descriptors, and existing studies show repeatability to be a good indicator of keypoint feature detector...
In this article, we propose a new optimized embedded architecture based soft-core processors oriented to visual attention based object recognition applications. Our recognition approach relies mainly on two specific modules for online processing of acquired images in real-time: a novel saliency based feature detector/descriptor module and then an object classifier module. To deal with such parallel/pipeline...
Video Summarization plays a vital role in the internet user's life, especially for those searching for user specified video of interest for a long time. In order to provide support for users in terms of searching and retrieving video content, it is necessary to segment the video into shots and extract representative frame of each shot which acts as a summary of the video. So, in this paper, an approach...
Visually challenged are majorly dependent on the Braille language for comprehensive reading of textual documents and their walking sticks they hold everyday for their obstruction identification. Making them virtually visible in an environment and get them workable in a technical organisation this system innovates the technology which provides audio descriptions of their blind surroundings. The design...
In this paper, we generated an activity recognition model using an ANN and trained it using Backpropagation learning. We considered a sandwich making scenario and identified the hand-motion-based activities of reaching, sprinkling, spreading and cutting. The contribution of this paper is twofold: First, given the fact that many image processing steps like feature identification are computation intensive...
This paper considers the problem of material recognition. Motivated by observation of close interconnections between material and object recognition, we study how to select and integrate multiple features obtained by different models of Convolutional Neural Networks (CNNs) trained in a transfer learning setting. To be specific, we first compute activations of features using representations on images...
This paper proposes the use of change detection in a multi-view object recognition system in order to improve its flexibility and effectiveness in dynamic environments. Multi-view recognition approaches are essential to overcome problems related to clutter, occlusion or camera noise, but the existing systems usually assume a static environment. The presence of dynamic objects raises another issue...
Computer Vision is a field which deals with extracting, analyzing, processing and understanding the images. One of the major application of computer vision is Object Recognition. In this paper, an algorithm is proposed where, object recognition requires two tasks: (i) Object Detection and (ii) Object Classification. The former task, extracts constructive information from the image and detects the...
With the recent emergence of low-cost depth sensors (e.g., Microsoft Kinect), RGB-D image can be captured more easily for object recognition. Compared to the existing RGB-based paradigm, the introduction of depth information indeed imports extra descriptive cues (e.g., surface geometry) for object characterization. In this paper, a novel hybrid RGB-D object categorization model is proposed. It is...
This paper surveys the learning algorithms of visual features representation and the computational modelling approaches proposed with the aim of developing better artificial object recognition systems. It turns out that most of the learning theories and schemas have been developed either in the spirit of understanding biological facts of vision or designing machines that provide better or competitive...
This paper presents an efficient approach to recognize objects captured with an RGB-D sensor. The proposed approach uses a Bag-of-Words (BOW) model to learn feature representations from raw RGB-D point clouds in a weakly supervised manner. To this end, we introduce a novel method based on randomized clustering trees to learn visual vocabularies which are fast to compute and more discriminative compared...
The computational models of visual attention, originally pro posed as cognitive models of human attention, nowadays are being used as front-ends to numerous vision systems like automatic object recognition. These systems are generally evaluated against eye tracking data or manually segmented salient objects in images. We previously showed that this comparison can lead to different rankings depending...
Object recognition, which consists of classification and detection, has two important attributes for robustness: (1) Closeness: detection windows should be close to object locations, and (2) Adaptiveness: object matching should be adaptive to object variations in classification. It is difficult to satisfy both attributes by considering classification and detection separately, thus recent studies combine...
In this paper, we propose a new 3D object recognition method. The method segments a 3D point set into a number of planar patches and extracts the Inter-Plane Relationships (IPRs) for all patches. Based on the IPRs, the method determines the High Level Feature (HLF) for each patch. A Gaussian-Mixture-Model-based plane classifier is then employed to classify each patch into one belonging to a certain...
Robots operating in human environments must have the ability to autonomously acquire object representations in order to perform object search and recognition tasks without human intervention. However, autonomous acquisition of object appearance model in an unstructured and cluttered human environment is a challenging task, since the object boundaries are unknown in prior. In this paper, we present...
Scale Invariant Feature Transform (SIFT) descriptor can represent the object in detail, and is robust to variations due to image scaling and illumination changes. The challenge of using such descriptor to perform image retrieval in a large scale database is the high computational complexity. In this paper, we present the bag of words model combined with SIFT to reduce the computation cost. The average...
Character recognition in scene images is an extremely challenging task. Although several techniques are reported performing well, they pertain to English only. This paper focuses on Devanagari character recognition from scene images. Devanagari script is very popular language and has very typical characteristics different from other scripts, particularly English. Combination of basic Devanagari consonants...
In this paper we apply the deformable part model by Felzenszwalb et al., which is at this moment the state of the art in many computer vision related tasks, to detect different types of man made structures in very high resolution aerial images — a reputedly difficult problem in our field. We test the framework on a database of crops of aerial images at a definition of 10 cm/pixel and investigate how...
The research community is experiencing nowadays a significant growth in the amount of sensor data made available to several practical applications, particularly those dealing with visual information. The availability of large datasets poses critical challenges for the selection of only relevant features to allow their timely use and interpretation. The recent years marked an increasing interest in...
This paper presents an object recognition method; feature X-D such as Kanade- Lucas-Tomasi Feature (KLT)-D and Speeded-Up Robust Features (SURF)-D. The main idea of the proposed algorithm is to use distance method to achieve rotation and position invariance. The anchor point, a center point of the target boundary region, is proposed in this paper as the basis of the pose estimation and it can be obtained...
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