This paper mainly proposes a line segment detection method based on pseudo peak suppression and local Hough transform, which has good noise resistance and can solve the problems of short line segment missing detection, false detection, and oversegmentation. In addition, in response to the phenomenon of uneven development in nuclear emulsion tomographic images, this paper proposes an image preprocessing process that uses the “Difference of Gaussian” method to reduce noise and then uses the standard deviation of the gray value of each pixel to bundle and unify the gray value of each pixel, which can robustly obtain the linear features in these images. The tests on the actual dataset of nuclear emulsion tomographic images and the public YorkUrban dataset show that the proposed method can effectively improve the accuracy of convolutional neural network or vision in transformer-based event classification for alpha-decay events in nuclear emulsion. In particular, the line segment detection method in the proposed method achieves optimal results in both accuracy and processing speed, which also has strong generalization ability in high quality natural images.

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

Hough-based voting approaches have been widely used to solve many detection problems such as object and action detection. These approaches for action detection cast votes for action classes and positions based on the local spatio-temporal features of given videos. The voting process of each local feature is performed independently of the other local features. This independence enables the method to be robust to occlusions because votes based on visible local features are not influenced by occluded local features. However, such independence makes discrimination of similar motions between different classes difficult and causes the method to cast many false votes. We propose a novel Hough-based action detection method to overcome the problem of false votes. The false votes do not occur randomly such that they depend on relevant action classes. We introduce vote distributions, which represent the number of votes for each action class. We assume that the distribution of false votes include important information necessary to improving action detection. These distributions are used to build a model that represents the characteristics of Hough voting that include false votes. The method estimates the likelihood using the model and reduces the influence of false votes. In experiments, we confirmed that the proposed method reduces false positive detection and improves action detection accuracy when using the IXMAS dataset and the UT-Interaction dataset.

A network-based remote host clock skew measurement involves collecting the offsets, the differences between sending and receiving times, of packets from the host within a period of time. Although the variant and immeasurable delay in each packet prevents the measurer from getting the real clock offset, the local minimum delays and the majority of delays delineate the clock offset shifts, and are used by existing approaches to estimate the skew. However, events during skew measurement like time synchronization and rerouting caused by switching network interface or base transceiver station may break the trend into multi-segment patterns. Although the skew in each segment is theoretically of the same value, the skew derived from the whole offset-set usually differs with an error of unpredictable scale. In this work, a method called dynamic region of offset majority locating (DROML) is developed to detect multi-segment cases, and to precisely estimate the skew. DROML is designed to work in real-time, and it uses a modified version of the HT-based method [8] both to measure the skew of one segment and to detect the break between adjacent segments. In the evaluation section, the modified HT-based method is compared with the original method and with a linear programming algorithm (LPA) on accumulated-time and short-term measurement. The fluctuation of the modified method in the short-term experiment is 0.6 ppm (parts per million), which is obviously less than the 1.23 ppm and 1.45 ppm from the other two methods. DROML, when estimating a four-segment case, is able to output a skew of only 0.22 ppm error, compared with the result of the normal case.

We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

This paper proposes a method of accurately detecting the boundary of narrow stripes, such as lane markings, by employing gradient cues of edge points. Using gradient direction cues, the edge points at the two sides of the boundary of stripes are classified into two groups before the Hough transform is applied to extract the boundary lines. The experiments show that the proposed method improves significantly the performance in terms of the accuracy of boundary detection of narrow stripes over the conventional approaches without edge point grouping.

An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.

Quantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

This paper describes the real-time implementation of a vision-based overtaking vehicle detection method for driver assistance systems using IMAPCAR, a highly parallel SIMD linear array processor. The implemented overtaking vehicle detection method is based on optical flows detected by block matching using SAD and detection of the flows' vanishing point. The implementation is done efficiently by taking advantage of the parallel SIMD architecture of IMAPCAR. As a result, video-rate (33 frames/s) implementation could be achieved.

This paper presents a target location estimation method that can use a pair of leaky coaxial cables to determine the 2D coordinates of the target. Since convention location techniques using leaky coaxial cables can find a target location along the cable in 1D, they have been unable to locate it in 2D planes. The proposed method enables us to estimate target on a 2D plane using; 1) a beam-forming technique and 2) a reconstruction technique based on Hough transform. Leaky coaxial cables are equipped with numerous slots at regular interval, which can be utilized as antenna arrays that acts both as transmitters and receivers. By completely exploiting this specific characteristic of leaky coaxial cables, we carried out an antenna array analysis that performs in a beam-forming fashion. Simulation and experimental results support the effectiveness of the proposed target location method.

We present the use of a Modified Generalized Hough Transform (MGHT) and deformable contours for image data retrieval where a given contour, gray-scale, or color template image can be detected in the target image, irrespective of its position, size, rotation, and smooth deformation transformations. Potential template positions are found in the target image using our novel modified Generalized Hough Transform method that takes measurements from the template features by extending a line from each edge contour point in its gradient direction to the other end of the object. The gradient difference is used to create a relationship with the orientation and length of this line segment. Potential matching positions in the target image are then searched by also extending a line from each target edge point to another end along the normal, then looking up the measurements data from the template image. Positions with high votes become candidate positions. Each candidate position is used to find a match by allowing the template to undergo a contour transformation. The deformed template contour is matched with the target by measuring the similarity in contour tangent direction and the smoothness of the matching vector. The deformation parameters are then updated via a Bayesian algorithm to find the best match. To avoid getting stuck in a local minimum solution, a novel coarse-and-fine model for contour matching is included. Results are presented for real images of several kinds including bin picking and fingerprint identification.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.

This paper presents a new fast and robust circle extraction method that is capable of extracting circles from images with complicated backgrounds. It is not based on the Hough transform (HT) that requires a time-consuming voting process. The proposed method uses a least-squares circle fitting algorithm for extracting circles. The arcs are fitted by extended digital lines that are extracted by a fast line extraction method. The proposed method calculates accurate circle parameters using the fitted arcs instead of evidence histograms in the parameter space. Tests performed on various real-world images show that the proposed method quickly and accurately extracts circles from complicated and heavily corrupted images.

This paper proposes a noise robust speech recognition method using prosodic information. In Japanese, the fundamental frequency (F0) contour represents phrase intonation and word accent information. Consequently, it conveys information about prosodic phrases and word boundaries. This paper first describes a noise robust F0 extraction method using the Hough transform, which achieves high extraction rates under various noise environments. Then it proposes a robust speech recognition method using multi-stream HMMs which model both segmental spectral and F0 contour information. Speaker-independent experiments are conducted using connected digits uttered by 11 male speakers in various kinds of noise and SNR conditions. The recognition error rate is reduced in all noise conditions, and the best absolute improvement of digit accuracy is about 4.5%. This improvement is achieved by robust digit boundary detection using the prosodic information.

This paper first presents robust algorithms to extract invariants of the linear Lie algebra model from 3D objects. In particular, an extended 3D Hough transform is presented to extract accurate estimates of the normal vectors. The Least square fitting is used to find normal vectors and representation matrices. Then an algorithm of segmentation for 3D objects is shown using the invariants of the linear Lie algebra. Distributions of invariants, both in the invariant space and on the object surface, are used for clustering and edge detection.

In screening for primary lung cancer with plain chest radiography, computer-aided diagnosis systems are being developed to reduce chest radiologists' task and the risk of missing positive cases. We evaluated a difference filter that enhances nodule densities in the preprocessing of chest X-ray images. Since ribs often affect detection of pulmonary nodules, we designed an eye-shaped filter to fit the rib shape. Although this filter increased the nodule detection rate, it could not detect nodules near the thoracic wall. The thoracic wall was then outlined by computers with Hough transformation for line detection. On the basis of the outline, the direction of the eye-shaped filter was determined. With this technique, the filter was not affected by considerable changes in the shape of anatomical structures, such as ribs and the thoracic wall, and could detect pulmonary nodules regardless of their location.

A large number of techniques have been proposed for acceleration of the Hough Transform, because the transformation is computationally very expensive in general. It is known that the sampling interval in parameter space is strongly related to the computation cost. The precision of the transformation and the processing speed are in a trade-off relationship. No fair comparison of the processing speed between various methods was performed in all previous works, because no criterion had been given for the sampling interval of parameter, and because the precision of parameter was not equal between methods. At the beginning of our research, we derive the relationship between the sampling interval and the precision of parameter. Then we derive a framework for comparing computation cost under equal condition for precision of parameter, regarding the total number of sampling points of a parameter as the computation cost. We define the transformation error in the Hough Transform, and the error is regarded as transformation noise. In this paper we also propose a design method called "Noise-level Shaping," by which we can set the transformation noise to an arbitrarily level. The level of the noise is varied according to the value of a parameter. Noise-level Shaping makes it possible for us to find the efficient parameterization and to find the efficient sampling interval in a specific application of the Hough Transform.

A method for locating underground pipes from a pulse radar image is presented. The method employs the Laplacian of Gaussian filter to extract edges and employs the Hough transform to determine the depth of the pipes. A preliminary experiment showed its ability to detect deeply buried pipes with weak signal echoes.

This paper describes a defect detection method which automatically extracts defect information from complicated background LSI patterns. Based on a scanning electron microscope (SEM) image, the defects on the wafer are characterized in terms of their locations, sizes and the shape of defects. For this purpose, two image processing techniques, the Hough transform and wavelet transform, have been employed. Especially, the Hough Transform for circles is applied to non-circular defects for estimating the shapes of defects. By experiments, it has been demonstrated that the system is very effective in defect identification and will be used as an integral part in future automatic defect pattern classification systems.

A function approximation scheme for image restoration is presented to resolve conflicting demands for smoothing within each object and differentiation between objects. Images are defined by probability distributions in the augmented functional space composed of image values and image planes. According to the fuzzy Hough transform, the probability distribution is assumed to take a robust form and its local maxima are extracted to yield restored images. This statistical scheme is implemented by a feedforward neural network composed of radial basis function neurons and a local winner-takes-all subnetwork.