<title>Expanded Dempster-Shafer reasoning technique for image feature integration and object recognition</title>

As an important branch of artificial intelligence, computer vision plays a huge role in the rapid development of artificial intelligence. From a biological point of view, in the acquisition and processing of information, vision is much more important than hearing, touch, etc., because 70% of the human cerebral cortex is processing visual information. Therefore, advances in computer vision technology are critical to the development of artificial intelligence that is designed to allow machines to think and handle things like humans. The acquisition and processing of visual information has always been the focus of computer vision research, and it is also difficult. The main problem of traditional computer vision technology in the processing of visual information is that the extracted image features are less discriminative, the generalization ability of image features in complex background scenes is insufficient, and the recognition ability on object recognition is poor. In response to these problems, based on the visual neural mechanism, this paper establishes an appropriate computer model for the neuronal cells in the human primary visual cortex, models the recognition response mechanism of the visual ventral system, and performs image feature extraction on the training samples. And object recognition. The results show that compared with the traditional methods, the proposed method effectively improves the discrimination of image features, and the image features extracted under complex background scenes have good generalization ability. On this basis, the training samples can be effectively recognized. The results show that the model based on the visual neural mechanism, the recognition of the edge, orientation and contour of the training sample show the advantages of the biological vision mechanism in object recognition.

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RELATIVE POSE ESTIMATION USING IMAGE FEATURE TRIPLETS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-w2-39-2015 ◽

2015 ◽

Vol XL-3/W2 ◽

pp. 39-45

Author(s):

T. Y. Chuang ◽

F. Rottensteiner ◽

C. Heipke

Keyword(s):

Pose Estimation ◽

Image Sequences ◽

Image Feature ◽

Lookup Table ◽

Feature Integration ◽

Image Point ◽

Routine Practice ◽

Adjustment Procedure ◽

Matching Process ◽

Relative Pose

A fully automated reconstruction of the trajectory of image sequences using point correspondences is turning into a routine practice. However, there are cases in which point features are hardly detectable, cannot be localized in a stable distribution, and consequently lead to an insufficient pose estimation. This paper presents a triplet-wise scheme for calibrated relative pose estimation from image point and line triplets, and investigates the effectiveness of the feature integration upon the relative pose estimation. To this end, we employ an existing point matching technique and propose a method for line triplet matching in which the relative poses are resolved during the matching procedure. The line matching method aims at establishing hypotheses about potential minimal line matches that can be used for determining the parameters of relative orientation (pose estimation) of two images with respect to the reference one; then, quantifying the agreement using the estimated orientation parameters. Rather than randomly choosing the line candidates in the matching process, we generate an associated lookup table to guide the selection of potential line matches. In addition, we integrate the homologous point and line triplets into a common adjustment procedure. In order to be able to also work with image sequences the adjustment is formulated in an incremental manner. The proposed scheme is evaluated with both synthetic and real datasets, demonstrating its satisfactory performance and revealing the effectiveness of image feature integration.

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