Image recognition is the process of comparing and identifying an object or a feature in a digital image or video. This concept is used in many applications, e.g., systems for factory automation, toll booth monitoring, and security surveillance. Color descriptors are employed to increase illumination invariance and discriminative power, but this technique when used in isolation does not lead to scale invariant image detection. SIFT, a feature detection mechanism, is scale invariant and is employed to improve search performance. The feature descriptors are clustered to derive a search index. When combined, these techniques provide improved matching to image queries
Local image features are used in many computer vision applications. Many point detectors and descrip...
In this paper, face recognition using the most representative SIFT images is presented. It is based ...
In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm...
In computer vision, determining the presence and placement of objects inside an image is known as ob...
Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinat...
Conventional pattern recognition systems have two components: feature analysis and pattern classific...
Scale-invariant feature transform (SIFT) is an algorithm to detect and describe local features in im...
Object Detection refers to the capability of computers and software to locate objects in an image/sc...
Clustering algorithms – a field of data mining – aims at finding a grouping structure in the input d...
In describing image features it is important to consider the fact that the appearance of a feature d...
Image category recognition is important to access visual information on the level of objects and sce...
There is a great deal of systems dealing with image processing that are being used and developed on ...
This paper presents a new approach to the detection of facial features. A scale adapted Harris Corne...
SIFT (scale invariant feature transform) being a feature extraction algorithm was initially used for...
Nowadays, many features have been proposed for image category recognition. Scale Invariant Feature T...
Local image features are used in many computer vision applications. Many point detectors and descrip...
In this paper, face recognition using the most representative SIFT images is presented. It is based ...
In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm...
In computer vision, determining the presence and placement of objects inside an image is known as ob...
Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinat...
Conventional pattern recognition systems have two components: feature analysis and pattern classific...
Scale-invariant feature transform (SIFT) is an algorithm to detect and describe local features in im...
Object Detection refers to the capability of computers and software to locate objects in an image/sc...
Clustering algorithms – a field of data mining – aims at finding a grouping structure in the input d...
In describing image features it is important to consider the fact that the appearance of a feature d...
Image category recognition is important to access visual information on the level of objects and sce...
There is a great deal of systems dealing with image processing that are being used and developed on ...
This paper presents a new approach to the detection of facial features. A scale adapted Harris Corne...
SIFT (scale invariant feature transform) being a feature extraction algorithm was initially used for...
Nowadays, many features have been proposed for image category recognition. Scale Invariant Feature T...
Local image features are used in many computer vision applications. Many point detectors and descrip...
In this paper, face recognition using the most representative SIFT images is presented. It is based ...
In this paper we propose a novel feature based on SIFT (Scale Invariant Feature Transform) algorithm...