Analysis and development of algorithm and method for object recognition
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Date
2025
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SDU University
Abstract
In an era characterized by the proliferation of visual data, the ability to comprehend and interpret the content of images and videos is of paramount significance [1]. Object recognition, a cornerstone of computer vision, plays a pivotal role in this endeavor. It constitutes the bedrock upon which numerous applications, ranging from autonomous vehicles to augmented reality systems, rely for accurate perception and decision-making. The fundamental task of object recognition is to endow machines with the capability to identify and categorize objects within a given visual context, akin to the cognitive abilities of the human visual system [2]. Pattern identification, description, categorization, and grouping provide challenges for Artificial Intelligence (AI), medicine, biology, and other branches of engineering and science. There are numerous definitions of the term "pattern". A pattern is a group of things, happenings, or thoughts that have certain similarities or features. According to Norbert Wiener, the essence of a pattern is an arrangement [3]. It is distinguished by the arrangement of its constituent pieces, rather than by their intrinsic characteristics the pattern is sometimes regarded as the opposite of chaos and a substantially namable item [4]. It can also be defined by a factor shared by multiple instances of the same object. Similarity in fingerprint pictures creates fingerprint patterns; handwriting, audio signals, web pages, and the human face are further examples of patterns [5]. Object recognition remains a challenging problem in computer vision due to various factors that affect the reliability and generalization of recognition systems. According to Bansal et al. in 2021, key challenges include the variability in object appearance, where differences in size, shape, color, illumination, and orientation make it difficult for models to learn consistent representations. Another major issue is scale and resolution, as detecting small objects within large and complex scenes requires algorithms capable of handling multi-scale information effectively. Partial occlusion also hinders detection accuracy when objects are partially hidden by others or by their own parts, while intra-class variability causes significant differences in appearance among objects of the same category. Moreover, Bansal et al. highlight that deep learning approaches often depend on large annotated datasets, and limited training data remain a major obstacle to achieving stable recognition performance [6]. Therefore, the present work focuses on addressing the limitations associated with insufficient data in object recognition tasks
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Keywords
Barriers to Large-Scale Data Collection, Projection-Based Classifier, ORL dataset, PCA-TP Recognition Framework
Citation
Aitimov A.K / Analysis and development of algorithm and method for object recognition / SDU University / 6D070400 – Computing Systems and Software