Corpus ID: 17024318

Techniques for Object Recognition in Images and Multi-Object Detection

@inproceedings{Khurana2013TechniquesFO,
  title={Techniques for Object Recognition in Images and Multi-Object Detection},
  author={Khushboo Khurana and Reetu Awasthi},
  year={2013}
}
The modern world is enclosed with gigantic masses of digital visual information. Increase in the images has urged for the development of robust and efficient object recognition techniques. Most work reported in the literature focuses on competent techniques for object recognition and its applications. A single object can be easily detected in an image. Multiple objects in an image can be detected by using different object detectors simultaneously. The paper discusses various techniques for… Expand
Object Detection System in Image Processing
An object detection system finds objects of the real world present either in a digital image or a video, where the object can belong to any class of objects namely humans, cars, etc. In order toExpand
A Review on the Performance of Object Detection Algorithm
TLDR
The overall goal of this research work is to propose an efficient and simple multiple object detection using the fuzzy and transition region based image segmentation. Expand
Object Detection using Template and HOG Feature Matching
TLDR
This paper investigates in detail another technique which is known as HOG (Histogram of Oriented Gradient) approach, which works well when the template image is cropped from the original one, which is not always invariant due to various transformations in the test images. Expand
Prototype analysis of different object recognition techniques in image processing
TLDR
The different techniques of contextual feature extraction with respect to picture information of object classification, categorization and recognition of scalability and optimizations in real time picture processing applications are reviewed. Expand
Object Detection by Feature Matching Method
Image matching is a fundamental aspect of computer vision, including object recognition or scene recognition. For the Human recognition system despite of different viewpoints and differences, it isExpand
Image Based Real Time Object Detection and Recognition in Image Processing
Object detection is the process of finding real-world objects such as faces, bicycles, and buildings in images or videos. Object detection algorithms typically use extracted features and learningExpand
Robust Target Detection in Optical Scene Based on Multiple Reference Images
TLDR
The proposed technique differs significantly from many recent target detection techniques, as it is based mainly on a voting process that select the best matches between the reference images and the scene image for enhancing the matching task. Expand
A Technical Assessment on License Plate Detection System
TLDR
This chapter presents assessment on different methods in detecting the license plate and discusses a case study on it. Expand
A novel method to recognize object in Images using Convolution Neural Networks
TLDR
This research aims to propose a new design and a methodology to support a system in order to recognize the object and give us the pop-up message in case the object is considered as a weapon and to measure the accuracy of the proposed system. Expand
A contemporary approach for object recognition based on spatial layout and low level features’ integration
TLDR
A new method to integrate the spatial layout with primitive features for object recognition and measure accuracy rate is proposed and it is shown that this method achieved 78% mean average precision image matching algorithms on Flickr dataset. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 24 REFERENCES
An Automatic Algorithm for Object Recognition and Detection Based on ASIFT Keypoints
  • Reza Oji
  • Mathematics, Computer Science
  • ArXiv
  • 2012
TLDR
This paper presents a perfect method for object recognition with full boundary detection by combining affine scale invariant feature transform (ASIFT) and a region merging algorithm, which is very efficient and powerful to recognize the object and detect it with high accuracy. Expand
Robust Real-time Object Detection
TLDR
A visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described, with the introduction of a new image representation called the “Integral Image” which allows the features used by the detector to be computed very quickly. Expand
Active object detection
TLDR
This article investigates an object detection method that employs active scanning and verifies whether the method reaches its goal for a real-world task of face detection that has been studied before in (Kruppa et al., 2003; Cristinacce and Cootes, 2003). Expand
Color attributes for object detection
TLDR
This paper proposes the use of color attributes as an explicit color representation for object detection and shows that this method improves over state-of-the-art techniques despite its simplicity. Expand
HOG based multi-stage object detection and pose recognition for service robot
TLDR
Experiments in real-world environments have shown that the proposed HOG-based multistage approach for object detection and object pose recognition for service robots is much more accurate than the detection method as it uses only multi-class detector. Expand
Robust Real-Time Face Detection
TLDR
A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Expand
Object detection by global contour shape
TLDR
A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations, which achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets. Expand
Object recognition and segmentation using SIFT and Graph Cuts
TLDR
This paper proposes a method of object recognition and segmentation using scale-invariant feature transform (SIFT) and graph cuts and thanks to this combination, both recognition and segmentsation are performed automatically under cluttered backgrounds including occlusion. Expand
Color-based object recognition
TLDR
It is shown that normalized color rgb, saturation S and hue H, and the newly proposed color models c 1 c 2 c 3 and l 1 l 2 l 3 are all invariant to a change in viewing direction, object geometry and illumination. Expand
Template Match Object Detection for Inertial Navigation Systems
This paper devoted to propose template match object detection for inertial navigation systems (INS). The proposed method is an image processing technique to improve the precision of the INS forExpand
...
1
2
3
...