Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

色相を保存するカラー画像強調法に関する研究

東京都市大学博士（工学）2022年度(令和4年)doctoral thesi

Sample supervised search centric approaches in geographic object-based image analysis

Sample supervised search centric image segmentation denotes a general method where quality segments are generated based on the provision of a selection of reference segments. The main purpose of such a method is to correctly segment a multitude of identical elements in an image based on these reference segments. An efficient search algorithm traverses the parameter space of a given segmentation algorithm. A supervised quality measure guides the search for the best segmentation results, or rather the best performing parameter set. This method, which is academically pursued in the context of remote sensing and elsewhere, shows promise in assisting the generation of earth observation information products. The method may find applications specifically within the context of user driven geographic object-based image analysis approaches, mainly in respect of very high resolution optical data. Rapid mapping activities as well as general land-cover mapping or targeted element identification may benefit from such a method. In this work it is suggested that sample supervised search centric geographic segment generation forms the basis of a set of methods, or rather a methodological avenue. The original formulation of the method, although promising, is limited in the quality of the segments it can produce – it is still limited by the inherent capability of the given segmentation algorithm. From an optimisation viewpoint, various structures may be encoded forming the fitness or search landscape traversed by a given search algorithm. These structures may interact or have an interplay with the given segmentation algorithm. Various method variants considering expanded fitness landscapes are possible. Additional processes, or constituents, such as data mapping, classification and post-segmentation heuristics may be embedded into such a method. Three distinct and novel method variants are proposed and evaluated based on this concept of expanded fitness landscapes

Primena inteligentnih sistema mašinske vizije autonomnog upravljanja železničkim vozilima

The railway is an important type of transport and has a significant economic impact on the industry and people's everyday life. Due to its capacities and complex infrastructure, it is necessary to work on its constant development and improvement. Railway automation requires the use of intelligent systems as a necessary part of an autonomous railway vehicle. As from the point of view of safe traffic, the existence of the object on the rail track and / or in its vicinity represents a potential obstacle to the railway traffic, and visibility has a very important role in correct and timely detection of the object on the railway infrastructure, a key element of autonomous railway vehicle is an obstacle detection system on the part of the railway infrastructure, in conditions of reduced visibility. The subject of scientific research of this doctoral dissertation is the application of intelligent machine vision systems in autonomous train operation. For the purpose of detecting obstacles on the part of the railway infrastructure in conditions of reduced visibility, a thermal imaging camera and a night vision system are integrated into the system, coupled with a developed advanced algorithm for image processing with artificial intelligence tools. In addition, the distance from the machine vision system to the detected object was estimated. The operation of the system was tested in a series of field experiments, at different locations, in different visibility conditions and weather conditions, through realistic scenarios