Chia-Hong Chang

Vehicle speed detection from a single motion blurred image

An image-based method for vehicle speed detection is presented. Conventional speed measurement techniques use radar- or laser-based devices, which are usually more expensive compared to a passive camera system. In this work, a single image captured with vehicle motion is used for speed measurement. Due to the relative motion between the camera and a moving object during the camera exposure time, motion blur occurs in the dynamic region of the image. It provides a visual cue for the speed measurement of a moving object. An approximate target region is first segmented and blur parameters are estimated from the motion blurred subimage. The image is then deblurred and used to derive other parameters. Finally, the vehicle speed is calculated according to the imaging geometry, camera pose, and blur extent in the image. Experiments have shown the estimated speeds within 5% of actual speeds for both local and highway traffic.

Automatic speed measurements of spherical objects using an off-the-shelf digital camera

A method for automatic speed measurements of spherical objects using a digital camera is presented. Conventional speed measurement techniques use radar or laser based devices, which are usually more expensive compared to a passive camera system. In this work, a single image captured with object motion is used to estimate the speed of a spherical object (such as baseball). Due to the relative motion between the camera and a moving object during the camera exposure time, motion blur occurs in the dynamic region of the image. By identifying the motion blur parameters, the speed of a moving object can be obtained. Automatic target identification and motion estimation are first done by motion blur analysis, followed by more accurate blur identification using circle fitting of the spherical object. Finally, the object speed is calculated according to the imaging geometry, camera pose, and blur extent in the image.

Depth Recovery from Motion Blurred Images

This paper presents a novel method to obtain the depth information from motion blurred images. Under the assumption of uniform linear motion between the camera and the scene during finite exposure time, both the pinhole model and the camera with a finite aperture are considered. It is shown that the image blur produced by lateral motion of the camera is inversely proportional to the distance of the object. Furthermore, if the speed of the relative motion is known, the depth of the object can be acquired by identifying the blur parameters. An image blur model for uniform linear motion is formulated based on geometric optics. The proposed method has been verified experimentally using edge images

Depth from motion and defocus blur

Finding the distance of an object in a scene from intensity images is an essential problem in many applications. In this work, we present a novel method for depth recovery from a single motion and defocus blurred image. Under the assumption of uniform lateral motion of the camera during finite exposure time, both the pinhole model and the camera with a finite aperture are considered. It is shown that the image blur produced by uniform linear motion of the camera is inversely proportional to the distance of the object. Furthermore, if the speed of the relative motion is known, the depth of the object can be acquired by identifying the blur parameters. An image blur model is formulated based on geometric optics. The blur extent is estimated by intensity profile analysis and focus measurement of the deblurred images. The proposed method is verified experimentally using different types of test patterns in an indoor environment.

Photo-Consistent motion blur modeling for realistic image synthesis

Motion blur is an important visual cue for the illusion of object motion. It has many applications in computer animation, virtual reality and augmented reality. In this work, we present a nonlinear imaging model for synthetic motion blur generation. It is shown that the intensity response of the image sensor is determined by the optical parameters of the camera and can be derived by a simple photometric calibration process. Based on the nonlinear behavior of the image intensity response, photo-realistic motion blur can be obtained and combined with real scenes with least visual inconsistency. Experiments have shown that the proposed method generates more photo-consistent results than the conventional motion blur model.

Photo-consistent synthesis of motion blur and depth-of-field effects with a real camera model

Depth-of-field (DOF) and motion blur are important visual cues used for computer graphics and photography to illustrate focus of attention and object motion. In this work, we present a method for photo-realistic DOF and motion blur generation based on the characteristics of a real camera system. Both the depth-blur relation for different camera focus settings and the nonlinear intensity response of image sensors are modeled. The camera parameters are calibrated and used for defocus and motion blur synthesis. For a well-focused real scene image, DOF and motion blur effects are generated by post-processing techniques. Experiments have shown that the proposed method generates more photo-consistent results than the commonly used graphical models.

Speed measurement of spherical objects using an off-the-shelf digital camera

Motion blur is a result of finite acquisition time of practical cameras and the relative motion between the camera and moving objects. We present a method for speed measurement of spherical objects using motion blurred images captured by a digital camera. The object is assumed under a straight line uniform-velocity motion, and the speed is calculated according to the imaging geometry and blur extent estimates. We have established a link between the motion blur information of a 2-D image and the speed information of a moving object. Experimental results are presented for the real-scene images.

Depth recovery from motion and defocus blur

Finding the distance of an object in a scene from intensity images is an essential problem in many applications. In this work, we present a novel method for depth recovery from a single motion and defocus blurred image. Under the assumption of uniform linear motion between the camera and the scene during finite exposure time, both the pinhole model and the camera with a finite aperture are considered. The blur extent is estimated by intensity profile analysis and focus measurement of the deblurred images. The proposed method has been verified experimentally using edge images.

Image retargeting approach for ranging cameras

Abstract This paper presents a novel image retargeting approach for ranging cameras. The proposed approach first extracts three feature maps: depth map, saliency map and gradient map. Then, the depth map and the saliency map are used to separate the main contents and the background and thus compute a map of saliency objects. After that, the proposed approach constructs an importance map which combines the four feature maps by the weighted sum. Finally, the proposed approach constructs the target image using the seam carving method based on the importance map. Unlike previous approaches, the proposed approach preserves the salient object well and maintains the gradients and visual effects in the background. Moreover, it protects the salient object from being destroyed by the seam carving algorithm. The experimental results show that the proposed approach performs well in terms of the resized quality.