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In order to effectively and accurately recognize students’ emotions in English teaching, and timely regulate students’ emotions, a method of emotion recognition and regulation in English teaching based on emotion computing technology is proposed. Through the skin color model, students’ facial images in the English teaching classroom obtained by the camera are searched for skin color regions, and students’ facial expression images in English teaching are detected, to carry out size normalization and grayscale normalization on the detected facial expression image, preprocess the facial expression image, use the binary method to locate the main facial organs of eyes and mouth that affect emotion in the preprocessed facial expression image, extract the edge features of facial expression image and the features of eyes and mouth, and take all the extracted features as the input of the model, output students’ emotion categories, and make corresponding teaching strategy adjustment and students’ emotion regulation according to students’ emotion categories, so as to finally realize English teaching emotion recognition and regulation. Experiments show that this method can effectively and fully detect the facial expression images of students learning English, and it is efficient for English teaching emotion recognition.

An efficient Hough transform algorithm on a reconfigurable mesh is proposed in this paper. For a problem with N edge pixels and an n×n parameter space, our algorithm runs in constant time on a 4-dimensional N×log²N×n×n reconfigurable mesh. The previous best algorithm for the same problem runs in a constant time on a 4-dimensional n×N×N×N reconfigurable mesh. Since n is always smaller than N in real world (in fact, n is in the order of N^1/2), our algorithm reduces the number of processors used drastically while keeping the same time complexity.

This paper presents a new technique for the recognition of hand-printed Latin characters using machine learning. Conventional methods have relied on manually constructed dictionaries which are not only tedious to construct but also difficult to make tolerant to variation in writing styles. The advantages of machine learning are that it can generalize over a large degree of variation between writing styles, and recognition rules can be constructed by example.

Characters are scanned into the computer and preprocessing techniques transform the bit-map representation of the characters into a set of primitives which can be represented in an attribute base form. A set of such representations for each character is then input to C4.5 which produces a decision tree for classifying each character.

Since networks of main roads are basic information for the classification of use of the earth surface, the automatic detection of roads from satellite images is a very important issue. In this paper, a new detection theory is proposed which can overcome drawbacks of current theories and detect plural roads in an image with high speed and high precision. Firstly, binary images representing edges are used to evaluate the possibility for a road to pass on a given pixel. An 8-directions-filter, a clearing filter and a parallel-edge-detection filter are proposed which can bring insufficient local information to each other to obtain global information enough to detect a road and by which the possibility of a road-passing on the pixel can be effectively evaluated. Secondly, by using the Hough transform and the optimal search method it is possible to detect a complete road. This detection theory does not depend on the size of image and can detect all the roads in an image including intersecting and T-type roads.

The main drawbacks of the Hough transform (HT) are the heavy requirement of computation and storage. To improve the drawbacks of the HT, the randomized Hough transform (RHT) was proposed. But the RHT is not suitable for detecting the pattern with the complex image because the probability is too low. In this paper, we propose a fast randomized Hough transform for circle/circular arc detection. We pick one point at random to be the seed point. Then, we propose a checking rule to confirm if the seed point is on the true circle. Compared with the previous techniques, the proposed method requires less computational time and is more suitable for complex images. In the experiments, synthetic and real images are used to show the effect of the proposed method.

Circles are important patterns in many automatic image inspection applications. The Hough Transform (HT) is a popular method for extracting shapes from original images. It was first introduced for the recognition of straight lines, and later extended to circles. The drawbacks of standard Hough Transform (SHT) for circle detection are the large computational and storage requirements. In this paper, we propose a modified HT called Vector Quantization of Hough Transform (VQHT) to detect circles more efficiently. The basic idea is to first decompose the edge image into many subimages by using Vector Quantization (VQ) algorithm based on their natural spatial relationships. The edge points resided in each subimage are considered as one circle candidate group. Then the VQHT algorithm is applied for fast circle detection. A new paradigm to store potential curve parameters is also proposed, which can exponentially reduce the storage space for HT algorithm. Experimental results show that the proposed algorithm can quickly and accurately detect multiple circles from the noisy background.

In the research of Tangut character recognition, it was attempted to detect the stroke of character with standard Hough transform (SHT), but hindered by the inability of SHT to detect the curve-line. In this paper, Hough transform with guidance of endpoints (HTGE) was proposed to achieve better performance on the aspect of curve and short line detection. HTGE is implemented based on the conception of hypothesis and verification, first, to assume there is line between each pair of endpoints, and then the existence of line is checked according to the corresponding area accumulated value in the parameter domain. With setting up of test object and evaluation criterion, the experiment was carried out to determine the key parameter for the best performance, and it can be concluded from the experiment result that considerable performance can be achieved with HTGE in the application of line detection.

As an infrastructure of biochemical laboratories, EP tube label plays a significant role in information extraction to meet the limitations of computing power in offline devices and solve the problem that the EP tube label cannot be accurately identified before identification because the label belongs to multi-angle random placement. This paper proposes a light-weight neural network YOLOv4-tiny-ECA to position tubes and a tilt correction method based on Hough transform. First, the EP tube rack is roughly positioned based on the diffuse filling algorithm combined with digital morphological corrosion, and then the EP tubes in the rack are precisely positioned using the light-weight YOLO target detection algorithm combined with the attention mechanism. Next, the baseline is added to the label as the basis for determining the tilt angle. For the valid target, the baseline is extracted using the Hough transform and the tilt angle is calculated by vector fork multiplication. Finally, baseline is removed using image processing algorithm for better recognition results. Our results show that the light-weight YOLO algorithm reduces the network parameters by 56% and computation by 55% while keeping the accuracy rate largely unchanged, the offline positioning tilt correction method can achieve 98.8% accuracy and 0.076s processing speed for a single test tube on average, which meets the real-time requirement.

This paper describes typical research on Chinese optical character recognition in Taiwan. Chinese characters can be represented by a set of basic line segments called strokes. Several approaches to the recognition of handwritten Chinese characters by stroke analysis are described here.

A typical optical character recognition (OCR) system consists of four main parts: image preprocessing, feature extraction, radical extraction and matching. Image preprocessing is used to provide the suitable format for data processing. Feature extraction is used to extract stable features from the Chinese character. Radical extraction is used to decompose the Chinese character into radicals. Finally, matching is used to recognize the Chinese character.

The reasons for using strokes as the features for Chinese character recognition are the following. First, all Chinese characters can be represented by a combination of strokes. Second, the algorithms developed under the concept of strokes do not have to be modified when the number of characters increases. Therefore, the algorithms described in this paper are suitable for recognizing large sets of Chinese characters.

This paper presents an accurate line extraction technique — the Hierarchical Peak Compaction Hough Transform (HPCHT). Vote scattering in the parameter space is a problem when the Hough transform is used for line extraction. This paper investigates the effects of image size and edge data errors on the severity of vote scattering. The HPCHT uses the Hough procedure on small subimages initially, and a recursive Hough merging scheme on the extracted line segments afterwards. A bound on vote scattering has been derived which guides the image subdivision and the adaptive quantization of the parameter space. As a result, an accurate Hough transform of low ρ-scattering and high θ-precision has been achieved. The HPCHT is suitable for fast parallel implementation on pyramid computers.

This paper considers the problem of detecting lines in images using a pyramid architecture. The approach is based on the Hough Transform calculation. A pyramid architecture of size n is a fine-grain architecture with a mesh base of size processors each holding a single pixel of the image. The pyramid operates in an SIMD mode. Two algorithms for computing the Hough Transform are explained. The first algorithm initially uses different angles, θ_j’s, and its complexity is O(k+log n) with O(m) storage requirement. The second algorithm computes the Hough Transform in a pipeline fashion for each angle θ_j at a time. This method produces results in O(k * log n) time with O(1) storage, where k is the number of θ_j angles, m is the number of ρ_i normal distances from the origin, and n is the number of pixels. A simulation program is also described.

The paper describes a method for detecting 2D straight segments and their correspondences in successive frames of an image sequence by means of a Hough-based matching approach. The main advantage of this method is the possibility of extracting and matching 2D straight segments directly in the feature space, without the need for complex matching operations and time-consuming inverse transformations. An additional advantage is that only four attributes of 2D straight segments are required to perform an efficient matching process: position, orientation, length, and midpoint. Tests were performed on both synthetic and real images containing complex man-made objects moving in a scene. A comparison with a well-known 2D line matching algorithm is also made.

Recognition of Chinese characters has been a major interest of researchers for many years, and a large number of research papers and reports have already been published in this area. There are several major problems: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to find.

This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variations in writing styles. The advantages of machine learning are twofold: it can generalize over the large degree of variations between writing styles and recognition rules can be constructed by example.

The paper also describes three methods of feature extraction for Chinese character recognition: regular expression, dominant point and modified Hough transform. These methods are then compared in terms of accuracy and efficiency.

In computerized radiography (CR) imaging, collimation is frequently employed to shield body parts from unnecessary radiation exposure and minimize radiation scattering using X-ray opaque material. The radiation field is therefore the diagnostic region of interest which has been exposed directly to X-rays. We present an image analysis system for the recognition of the collimation, or equivalently, detection of the radiation field. The purpose is to (1) facilitate optimal tone scale enhancement, which can be driven only by the diagnostically useful part of the image data, and (2) minimize the viewing flare caused by the unexposed area. This system consists of three stages of operations: (1) pixel-level detection and classification of collimation boundary transition pixels; (2) line-level delineation of candidate collimation blades; and (3) region-level determination of the collimation configuration. This system has been reduced to practice and tested over 807 images of 11 exam types and a success rate in excess of 99% has been achieved for tone scale enhancement and masking. Due to the novel design of the system, its computational efficiency lends itself to online operations.

This paper introduces a new discrete Hough transform, DHT, that pre-computes discrete line information (rules) and uses this information to detect line segments in the image. Pre-computing line information removes the need for run-time line calculations and the associated parameters. The proposed approach does not depend on the parameterization of a straight line and is formulated based on the discrete domain. This new DHT is compared with selected existing techniques to demonstrate the large reduction in computation time achieved by this new approach, while not sacrificing accuracy.

In this paper, we describe a new algorithm for radar detection based on the Hough transform which employs the slope-intercept parameter space. Unlike the conventional Hough transform, we shift the parameter space cells to perform the transform. The computation burden is reduced. Another advantage is that those straight lines whose intercept are bigger than the radar maximum range can also be detected. In addition, we also investigate the performance of the algorithm we present and show its efficiency with some simulations.

Visual illusion is the fallacious perception of reality or some actually existing object. In this paper, we imitate the mechanism of Ehrenstein illusion, neon color spreading illusion, watercolor illusion, Kanizsa illusion, shifted edges illusion, and hybrid image illusion using the Open Source Computer Vision Library (OpenCV). We also imitate these illusions using Cellular Neural Networks (CNNs). These imitations suggest that some illusions are processed by high-level brain functions. We next apply the morphological gradient operation to anomalous motion illusions. The processed images are classified into two kinds of images, which correspond to the central drift illusion and the peripheral drift illusion, respectively. It demonstrates that the contrast of the colors plays an important role in the anomalous motion illusion. We also imitate the anomalous motion illusions using both OpenCV and CNN. These imitations suggest that some visual illusions may be processed by the illusory movement of animations.

In this paper, we present a new method for Lane Detection (LD) to reduce the impact of some issues associated with Autonomous Driving Cars (ADCs). It relies on the fact that self-driving will be the getaway of the transportation future. The ADC technology has expanded as it does not require human assistance. For ACD to be fully utilized, Machine Learning (ML) tools, such as Deep Neural Networks (DNNs), are required. However, ACD technology uses tools like a camera, GPS, Ultrasonic Sensor (US), and others, which work together to produce accurate ACDs. While adopting ACD, several issues need to be addressed such as carves, blurry, unclean, shadow, sunlight, pedestrians, and weather conditions. The paper proposes a new method to detect when a portion of a road line is missing due to weather conditions or old marking, protecting pedestrians and cars. For lane detection, the DNN model and Hough transform are used. CULane datasets including training and testing samples were used in the experiments. The results show high accuracy levels of 92%, indicating the ability to detect road lanes.

Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flat-bed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realizing a practical document reader. This paper presents the use of analyzing the connected components extracted from the binary image of a document page. Such an analysis provides a lot of useful information, and will be used to perform skew correction, segmentation and classification of the document. Moreover, we describe two new algorithms — one for skew detection and one for skew correction. The new skew correction algorithm we propose has been shown to be fast and accurate, with run times averaging under 1.5 CPU seconds and 30 seconds real time to calculate the angle on a 5000/20 DEC workstation. Experiments on over 100 pages show that the method works well on a wide variety of layouts, including sparse textual regions, mixed fonts, multiple columns, and even for documents with a high graphical content.

To use the Hough transform to detect shapes we need to accumulate votes for the edge passing a specific bin. Most existing Hough transform techniques use a sharp (crisp) cutoff to determine whether the bin has received a vote or not. This results in considerable errors. In this paper, we propose a new line Hough transform (LHT) using evidence accumulation and fuzzy aggregation function. The resulting voting process is dependent on the distance ρ from the grid centers. This effectively handles uncertainty in the accumulation process and achieves a better performance. To show the effectiveness this approach, we present our experimental results for a set of 2D parametric and 3D nonparametric objects.