Feature-based Object Recognition Research Articles

Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Object recognition and tracking are two of the most dynamic research sub-areas that belong to the field of Computer Vision. Computer vision is one of the most active research fields that lies at the intersection of deep learning and machine vision. This paper presents an efficient ensemble algorithm for the recognition and tracking of fixed shape moving objects while accommodating the shift and scale invariances that the object may encounter. The first part uses the Maximum Average Correlation Height (MACH) filter for object recognition and determines the bounding box coordinates. In case the correlation based MACH filter fails, the algorithms switches to a much reliable but computationally complex feature based object recognition technique i.e., affine scale invariant feature transform (ASIFT). ASIFT is used to accommodate object shift and scale object variations. ASIFT extracts certain features from the object of interest, providing invariance in up to six affine parameters, namely translation (two parameters), zoom, rotation and two camera axis orientations. However, in this paper, only the shift and scale invariances are used. The second part of the algorithm demonstrates the use of particle filters based Approximate Proximal Gradient (APG) technique to periodically update the coordinates of the object encapsulated in the bounding box. At the end, a comparison of the proposed algorithm with other state-of-the-art tracking algorithms has been presented, which demonstrates the effectiveness of the proposed algorithm with respect to the minimization of tracking errors.

Design and Implementation of Artificial Intelligence Image Detection System Based on Noise Monitoring

Image quality detection technology can replace manual inspections to automatically detect video quality, accurately analyze, judge and alarm abnormalities in video images in the surveillance system to ensure the normal operation of the expanding network video surveillance system. The thesis designs an intelligent traffic image sensor system to realize fast motion detection and object recognition of the surveillance scene. This system introduces a background model based on region segmentation and a feature-based object recognition algorithm. Experimental results show that the system can perform automatic movement detection and object classification and recognition efficiently in real time.

Underwater Object Recognition Using Point-Features, Bayesian Estimation and Semantic Information.

This paper proposes a 3D object recognition method for non-coloured point clouds using point features. The method is intended for application scenarios such as Inspection, Maintenance and Repair (IMR) of industrial sub-sea structures composed of pipes and connecting objects (such as valves, elbows and R-Tee connectors). The recognition algorithm uses a database of partial views of the objects, stored as point clouds, which is available a priori. The recognition pipeline has 5 stages: (1) Plane segmentation, (2) Pipe detection, (3) Semantic Object-segmentation and detection, (4) Feature based Object Recognition and (5) Bayesian estimation. To apply the Bayesian estimation, an object tracking method based on a new Interdistance Joint Compatibility Branch and Bound (IJCBB) algorithm is proposed. The paper studies the recognition performance depending on: (1) the point feature descriptor used, (2) the use (or not) of Bayesian estimation and (3) the inclusion of semantic information about the objects connections. The methods are tested using an experimental dataset containing laser scans and Autonomous Underwater Vehicle (AUV) navigation data. The best results are obtained using the Clustered Viewpoint Feature Histogram (CVFH) descriptor, achieving recognition rates of , and , respectively, clearly showing the advantages of using the Bayesian estimation ( increase) and the inclusion of semantic information ( further increase).

Image Processing and Artificial Neural Network for Robot Application

This paper presents a robot vision application, implemented in MATLAB working environment, developed for feature-based object recognition, object sorting and manipulation, based on shape classification and its pose calculus for proper positioning. The application described in this article, designed to detect, identify, classify and manipulate objects is based on previous robot vision applications that are presented in more detail in [1]. The idea underlying the mentioned applications is to determine the type, position and orientation of the work pieces (in those cases different types of bearings). Taking it further, in the presented application, objects that show shape with a gradual level of complexity are used. For this reason pattern recognition are discriminated by training a two layers neural network. The network is presented and also the input and output vectors.

Local Kernel Feature Analysis (LKFA) for object recognition

This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.

Gabor Wavelet Selection and SVM Classification for Object Recognition

This paper proposes a Gabor wavelets and support vector machine (SVM)-based framework for object recognition. When discriminative features are extracted at optimized locations using selected Gabor wavelets, classifications are done via SVM. Compared to conventional Gabor feature based object recognition system, the system developed in this paper is both robust and efficient. The proposed framework has been successfully applied to two object recognition applications, i.e., object/non-object classification and face recognition. Experimental results clearly show advantages of the proposed method over other approaches.

A Precise Visual Control Method for Micromanipulator

A feature-based object recognition algorithm, which is robust against occlusion, is presented. The subpixel accuracy can be obtained by minimizing the sum of the distance between corresponding pixels of template and image. In order to avoid the iterative adjustment and complicated inverse solution of image jacobian, based on CMAC (Cerebellar Model Articulation Controller) neural network, a feedforward visual servo scheme is used to implement the mapping from the error signal in the image space to the control signal in the task space. Experimental result shows that precise positioning accuracy can be obtained by using this method.

Feature-Based Object Recognition and Localization in 3D-Space, Using a Single Video Image

We introduce a robust algorithm to recognize objects in 3D space from one 2D video image and to localize the objects in all six degrees of freedom. Point-like attached features are used in the input image and additional edge information provides grouping. In an initial phase, a 3D model of all objects to be recognized is stored in the computer represented by their features. Combining the location of the detected features in the 2D input scene with the features of the 3D computer model, each single feature gives a subspace as possible solutions of the location parameters to be determined. The points of intersection of the corresponding trajectories are accumulated as possible solutions in a Hough table. The location of the highest peak in the space of hypothetical solutions delivers the desired rotation and translation parameters, even for partially hidden objects. The fully analytical algorithm is adapted to weak perspective (orthographic and scale) as well as to perspective projection. An application to range images leads to the automated feature modeling of the required 3D reference objects.

Statistical Approaches to Feature-Based Object Recognition

This paper examines statistical approaches to model-based object recognition.Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform d...