Simple Vision System for Apple Varieties Classification

AbstractEvery variety of apple has its particular physical characteristics, which are affected by different pre-harvest factors. Manual classification of these varieties by human labor has several weaknesses, such as the inconsistency, subjectivity, fatigue and different accuracy due to different level of experience of the inspector. This study was aimed to design and evaluate a simple computer-based vision system for recognizing and grading several varieties of apples based on their physical characteristics. Images of apples were taken and were used as training data with different algorithms to extract the particular characteristics of each variety, such as color and shape. The extracted Hue color channels and contour vector were recorded as the reference data and were used to recognize the similar characteristic of those images from the testing data group. The k-nearest neighbors algorithm was used to decide whether an apple belongs to a particular variety. The results show that the recognition rate based on color only was between 84–97% and it was between 5–77% it is based on the shape only. Rotating the image significantly increases the recognition rate (to be between 5 - 69% based on the shape only). Moreover, combining both color and shape characteristics significantly improves the recognition rate.Keywords: apple’s varieties classification, color signatures, combined color-morphology signatures, morphology signature, vision system AbstrakSetiap jenis buah apel memiliki penciri fisik spesifik, yang dipengaruhi oleh berbagai faktor pra-panen. Teknik klasifikasi manual memiliki banyak kelemahan, antara lain adalah subjektifitas, ketidakkonsistenan, kelelahan fisik dan psikologis, serta tingkat pengalaman dari petugas yang melakukannya. Tujuan studi ini adalah melakukan proses desain dan pengujian suatu sistem visi sederhana berbasis komputer untuk mengenali dan mengklasifikasi berbagai jenis buah apel berdasarkan penciri spesifiknya. Citra buah apel dari sampel latih diproses dengan berbagai algoritma untuk mengekstraksi berbagai parameter pencirinya, yaitu parameter warna dan bentuk. Informasi histogram kanal warna Hue dan vektor kontur hasil ekstraksi kemudian disimpan sebagai data referensi dan digunakan sebagai pembanding terhadap parameter serupa dari citra data uji. Keputusan diambil menggunakan algoritma K-Nearest Neighbors. Hasil menunjukkan bahwa laju pengenalan berbasis fitur tunggal warna berkisar antara 84–97%, sementara berbasis fitur tunggal morfologi berkisar antara 5–77%. Perubahan orientasi sampel sebagai data training akan meningkatkan laju pengenalan berbasis fitur tunggal morfologi secara signifikan, yaitu dari 5% menjadi 69%. Penggabungan dua fitur penciri warna dan morfologi dapat meningkatkan laju pengenalan lebih baik lagi.Kata Kunci: klasifikasi jenis buah apel, penciri warna, penciri morfologi, gabungan penciri warna dan morfologi, sistem vis

Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems

Optical Character Recognition Using Morphological Attributes.

This dissertation addresses a fundamental computational strategy in image processing hand written English characters using traditional parallel computers. Image acquisition and processing is becoming a thriving industry because of the frequent availability of fax machines, video digitizers, flat-bed scanners, hand scanners, color scanners, and other image input devices that are now accessible to everyone. Optical Character Recognition (OCR) research increased as the technology for a robust OCR system became realistic. There is no commercial effective recognition system that is able to translate raw digital images of hand written text into pure ASCII. The reason is that a digital image comprises of a vast number of pixels. The traditional approach of processing the huge collection of pixel information is quite slow and cumbersome. In this dissertation we developed an approach and theory for a fast robust OCR system for images of hand written characters using morphological attribute features that are expected by the alphabet character set. By extracting specific morphological attributes from the scanned image, the dynamic OCR system is able to generalize and approximate similar images. This generalization is achieved with the usage of fuzzy logic and neural network. Since the main requirement for a commercially effective OCR is a fast and a high recognition rate system, the approach taken in this research is to shift the recognition computation into the system\u27s architecture and its learning phase. The recognition process constituted mainly simple integer computation, a preferred computation on digital computers. In essence, the system maintains the attribute envelope boundary upon which each English character could fall under. This boundary is based on extreme attributes extracted from images introduced to the system beforehand. The theory was implemented both on a SIMD-MC\sp2 and a SISD machine. The resultant system proved to be a fast robust dynamic system, given that a suitable learning had taken place. The principle contributions of this dissertation are: (1) Improving existing thinning algorithms for image preprocessing. (2) Development of an on-line cluster partitioning procedure for region oriented segmentation. (3) Expansion of a fuzzy knowledge base theory to maintain morphological attributes on digital computers. (4) Dynamic Fuzzy learning/recognition technique

Design of CT pictures from 2D to 3D

Volume visualization is one important part of scientific visualization. It has developed basing on absorbing the relative knowledge of computer delineation, computer visualization and computer image disposal. The knowledge of this branch is of much importance in computer application. Since it deals with contents with deeper meaning and it is more theoretic, having more arithmetic means, it generally stands for the level of computer application. The study and application of volume visualization is like a raging fire. My country started comparatively later in this field. This thesis gives systematic representation and discuss in the field of tomography image 3D reconstruction. It mainly discusses after rotation, translation, filtering, interpolation and sharpening a series of 2D CT scanning images, get the boundary data of different object to form 3D volume data and actualize the 3D reconstruction of the object, and at the same time implement the function of display, translation, rotation, scaling and projection the volume data. Basing on the implementation of these functions according to software programming, this thesis gives a sum up to each algorithm of 3D volume visualization processing. The method to actualize the 3D reconstruction of the tomography image is mainly about the image processing, image transformation, the way to actualize 3D reconstruction and image compression, etc. In image processing, it talks about getting the anchor points in the tomography image, the geometrie transformation of the image, getting the boundary of the target, cross section display and the smoothing and sharpening of the image. In the transformation of the image, this thesis deals with the algorithm and implementation principle of the geometric transformation (transition, rotation, and scaling) of the 2D image, the three-dimensionalization of the planar data, construction of the stereo mode, geometric transformation of the 3D graph, curve-fitting, the processing of hidden line and hidden surface, color processing. It also introduces the thoughts of using OpenGL to develop and actualize tomography image 3D reconstruction system, including using OpenGL to transform the coordinate, solid model building, to actualize 3D rotation and projection. Recently, the methods of applying chemotherapy to deal with cancer in hospitals of our country are different. Hospital with great fund takes import software to design while most of the hospitals take domestic software. These kinds of software are designed by DAHENG Company in BeiJing, WEIDA Company in ShenZhen. The programs in the software these two hospitals' doctors use to treat are images fielding in the plane not making radiation design under the mode of omnibearing cubic display. Under this circumstance the judgment of the key part can not remain precise, and this part is the most important part that the doctors need. The above problem is the aim of this project. This thesis mainly deals with the subject that after calibrating and sharpening the series of 2D CT images, extract the boundary data of different bodies to form a 3D volume data and actualize 3D reconstruction and at the same time actualize the function of display, translation, rotation, scaling and projection. Mostly basing on the application of medical area, this thesis aims at making further research on computer graphies, computer vision and computer image processing through the study and application of volume visualization in this field. By the study and development of the volume visualization technology in this project, it can provide simulation and display functions to the observer before the operation and the radiotherapy as well as providing the chance to simulate the real teaching and practicing link to the medical school in the teaching process, and increase the clinic level and teaching level of medical area.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : volume visualization, volume data, tomography image, 3D reconstruction, anchor point, boundary data, periphery boundary, OpenGL

Human Perception-Inspired Grain Segmentation Refinement Using Conditional Random Fields

Accurate segmentation of interconnected line networks, such as grain boundaries in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating intricate post-processing for effective contour closure and continuity. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique, leveraging domain knowledge about grain boundary connectivity and employing conditional random fields and perceptual grouping rules. This approach significantly enhances segmentation mask accuracy, achieving a 79% segment identification accuracy in validation with a U-Net model on electron microscopy images of a polycrystalline oxide. Additionally, a novel grain alignment metric is introduced, showing a 51% improvement in grain alignment, providing a more detailed assessment of segmentation performance for complex microstructures. This method not only enables rapid and accurate segmentation but also facilitates an unprecedented level of data analysis, significantly improving the statistical representation of grain boundary networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential

Image reconstruction from incomplete information

Imperial Users onl

A non-invasive technique for burn area measurement

The need for a reliable and accurate method for assessing the surface area of burn wounds currently exists in the branch of medicine involved with burn care and treatment. The percentage of the surface area is of critical importance in evaluating fluid replacement amounts and nutritional support during the 24 hours of postburn therapy. A noninvasive technique has been developed which facilitates the measurement of burn area. The method we shall describe is an inexpensive technique to measure the burn areas accurately. Our imaging system is based on a technique known as structured light. Most structured light computer imaging systems, including ours, use triangulation to determine the location of points in three dimensions as the intersection of two lines: a ray of light originating from the structured light projector and the line of sight determined by the location of the image point in the camera plane. The geometry used to determine 3D location by triangulation is identical to the geometry of other stereo-based vision system, including the human vision system. Our system projects a square grid pattern from 35mm slide onto the patient. The grid on the slide is composed of uniformly spaced orthogonal stripes which may be indexed by row and column. Each slide also has square markers placed in between time lines of the grid in both the horizontal and vertical directions in the center of the slide. Our system locates intersections of the projected grid stripes in the camera image and determines the 3D location of the corresponding points on the body by triangulation. Four steps are necessary in order to reconstruct 3D locations of points on the surface of the skin: camera and projector calibration; image processing to locate the grid intersections in the camera image; grid labeling to establish the correspondence between projected and imaged intersections; and triangulation to determine three-dimensional position. Three steps are required to segment burned portion in image: edge detection to get the strongest edges of the region; edge following to form a closed boundary; and region filling to identify the burn region. After combining the reconstructed 3D locations and segmented image, numerical analysis and geometric modeling techniques are used to calculate the burn area. We use cubic spline interpolation, bicubic surface patches and Gaussian quadrature double integration to calculate the burn wound area. The accuracy of this technique is demonstrated The benefits and advantages of this technique are, first, that we don’t have to make any assumptions about the shape of the human body and second, there is no need for either the Rule-of-Nines, or the weight and height of the patient. This technique can be used for human body shape, regardless of weight proportion, size, sex or skin pigmentation. The low cost, intuitive method, and demonstrated efficiency of this computer imaging technique makes it a desirable alternative to current methods and provides the burn care specialist with a sterile, safe, and effective diagnostic tool in assessing and investigating burn areas