In this paper, we propose a simple way to utilize stereo camera data to improve feature descriptors. Computer vision algorithms that use a stereo camera require some calculations of 3D information. We leverage this pre-calculated information to improve feature descriptor algorithms. We use the 3D feature information to estimate the scale of each feature. This way, each feature descriptor will b...

Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. Other recognition systems don’t nullify most of the lighting variations. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching and kernel based...

We address the problem of large scale image retrieval in a wide-baseline setting, where for any query image all the matching database images will come from very different viewpoints. In such settings traditional bag-of-visual-words approaches are not equipped to handle the significant feature descriptor transformations that occur under large camera motions. In this paper we present a novel appr...

SIFT (Scale invariant feature transform) descriptor is one of the most effective local features that is used for scale, rotation and illumination invariant of automatic image registration. In this paper a fast bi-directional matching algorithm based on SIFT was proposed for improving match accuracy and reducing match time. Firstly the single-feature points and multi-feature points in two images...

We describe a visual recognition system operating on a hand-held device, based on a video-based feature descriptor, and characterize its invariance and discriminative properties. Feature selection and tracking are performed in real-time, and used to train a template-based classifier during a capture phase prompted by the user. During normal operation, the system scores objects in the field of v...

background: one of the challenges of pet/mri combined systems is to derive an attenuation map to correct the pet image. for that, the pseudo-ct image could be used to correct the attenuation. until now, most existing scientific researches construct this pseudo-ct image using the registration techniques. however, these techniques suffer from the local minima of the non-rigid deformation energy f...

BACKGROUND Histologic image analysis plays an important role in cancer diagnosis. It describes the structure of the body tissues and abnormal structure gives the suspicion of the cancer or some other diseases. Observing the structural changes of these chaotic textures from the human eye is challenging process. However, the challenge can be defeat by forming mathematical descriptor to represent ...

In this paper, a new texture descriptor named "Fractional Local Neighborhood Intensity Pattern" (FLNIP) has been proposed for content based image retrieval (CBIR). It is an extension of the Local Neighborhood Intensity Pattern (LNIP)[1]. FLNIP calculates the relative intensity difference between a particular pixel and the center pixel of a 3×3 window by considering the relationship with adjacen...

As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develo...

This paper addresses the problem of object-based image retrieval, by using local feature extraction and a relevance feedback mechanism for quickly narrowing down the image search process to the user needs. This approach relies on the hypothesis that semantically similar images are clustered in some feature space and, in this scenario: (i) computes image signatures that are invariant to scale an...