Identification of Precise Object among Various Objects using Sparse Coding

  title={Identification of Precise Object among Various Objects using Sparse Coding},
  author={Giby Jose and P. Manimegalai},
  journal={International Journal of Computer Applications},
  • G. Jose, P. Manimegalai
  • Published 17 November 2016
  • Computer Science
  • International Journal of Computer Applications
In order to identify the exact coconut object form the image, the following methodology is proposed. The input image is preprocessed. Its quality is enhanced using histogram equalization to produce a better result for region-based feature extraction. The edges are then detected in the image segmentation process, as this information is most essential for the classifier algorithm. After detecting the edges the CHT algorithm is applied to identify the coconut. The performance measures viz… 

Particle swarm optimization for coconut detection in a coconut tree plucking robot

An image processing with particle swarm optimization (PSO) method is introduced and results show that successful rate of the method to detect coconuts at the tree with cluttered background is 80% and then pluck them using the robot arm.

Number 10

Qualitative/quantitative measurement of software using cluster analysis is discussed, including all the three associated in the list of seven factors with software reliability performance.



Robust Facial Expression Recognition via Compressive Sensing

Experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks.

A Robust Face Recognition System via Accurate Face Alignment and Sparse Representation

A robust real-time facial recognition system featuring a new cascade framework including two different methods for eye detection and face alignment, and a new approach for face detection termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA).

Face Recognition Based on Multi-classifierWeighted Optimization and Sparse Representation

The experiments show that the WMSRC algorithm outperforms many existing block-based sparse representation classification algorithms, especially for FR when the available training samples per subject are very limited.

On robust face recognition via sparse coding: the good, the bad and the ugly

Thorough experiments show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems.

Object Detection using Circular Hough Transform

The proposed system first uses the separability filter proposed by Fukui and Yamaguchi to obtain the best object candidates and next, the system uses the Circular Hough Transform (CHT) to detect the presence of circular shape.

Sparse Representation Approach for Variation-Robust Face Recognition Using Discrete Wavelet Transform

This paper is using sparse representation app roach based on discrete wavelet transform (DWT) to achieve more robustness to variation in light ing, directions and expressions, because sparse representation does not exterminate obstacles posed by several practical issues.

Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary

Experimental results on the AR and FERET databases show that ESRC has better generalization ability than SRC for undersampled face recognition under variable expressions, illuminations, disguises, and ages.

Subject Adaptive Affection Recognition via Sparse Reconstruction

Experimental results demonstrate that the proposed affection recognition framework can increase the discriminative power especially for facial expressions, and joint recognition strategy is demonstrated that it can utilize complementary information in both models so that to reach better recognition rate.

Robust face recognition using locally adaptive sparse representation

  • Yi ChenT. DoT. Tran
  • Computer Science
    2010 IEEE International Conference on Image Processing
  • 2010
A block-based face-recognition algorithm based on a sparse linear-regression subspace model via locally adaptive dictionary constructed from past observable data (training samples) that provides an immediate benefit — the increase in robustness level to various registration errors.

Robust Face Recognition via Sparse Representation

This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.