KEYWORDS: 3D displays, Signal to noise ratio, Detection and tracking algorithms, Target detection, Infrared detectors, Infrared radiation, Infrared imaging, Image processing, 3D acquisition, Optical engineering
Dim targets are extremely difficult to detect using methods based on single-frame detection. Radiation accumulation is one of the effective methods to improve signal-to-noise ratio (SNR). A detection approach based on radiation accumulation is proposed. First, a location space and a motion space are established. Radiation accumulation operation, controlled by vectors from the motion space, is applied to the original image space. Then, a new image space is acquired where some images have an improved SNR. Second, quasitargets in the new image space are obtained by constant false-alarm ratio judging, and location vectors and motion vectors of quasitargets are also acquired simultaneously. Third, the location vectors and motion vectors are mapped into the two spaces, respectively. Volume density function is defined in the motion space. Location extremum of the location space and volume density extremum of motion space will confirm the true target. Finally, actual location of the true target in the original image space is obtained by space inversion. The approach is also applicable to detect multiple dim targets. Experimental results show the effectiveness of the proposed approach and demonstrate the approach is superior to compared approaches on detection probability and false alarm probability.
Strong noises interference is a difficult technical problem for signals detection. Multiple targets detection with strong
noises makes the problem more complicated. Aiming at the difficulty of multiple uniform rectilinear motion targets
detection in infrared (IR) image sequences with strong noises, this paper presents a multiple dim targets detection
algorithm which improves signal-to-noise ratio (SNR). Firstly, we establish a velocity space and stack image sequences
along different velocity vectors. Secondly, mean filtering in time-domain is applied to stacked images. Thirdly,
quasi-target points in mean filtering images are selected by constant false-alarm ratio (CFAR) judging. Finally,
coordinate vectors and velocity vectors of quasi-target points are mapped to location space and velocity space,
respectively. As a result, local peaks from the two spaces will confirm target points; meanwhile, velocity vectors of
targets can also be acquired. In addition, effect of velocity steps on SNR improvement is analyzed, which can guide the
selection of steps and reduce computational burden. Both moving dim targets simulation experiment and real-world dim
targets detection experiment have proved that this algorithm can effectively detect multiple dim targets under strong
The traditional Hausdorff measure, which uses Euclidean distance metric (L2 norm) to define the distance between coordinates of any two points, has poor performance in the presence of the rotation and scale change although it is robust to the noise and occlusion. To address the problem, we define a novel similarity function including two parts in this paper. The first part is Hausdorff distance between shapes which is calculated by exploiting shape context that is rotation and scale invariant as the distance metric. The second part is the cost of matching between centroids. Unlike the traditional method, we use the centroid as reference point to obtain its shape context that embodies global information of the shape. Experiment results demonstrate that the function value between shapes is rotation and scale invariant and the matching accuracy of our algorithm is higher than that of previously proposed algorithm on the MEPG-7 database.