Feature Points Hack Fix
Sharmaine Kass
This a very common problem. Because images like this, they actually do not have much in common. The overlap region is not rich in feature. What you can do is dig into opencv stitcher code and there they use confidence factor for feature matching, you can play with that confidence factor to get matches in this case. But this will only work if your feature detector is able to detect some features in overlapping resion.
This is full pipleline of OPencv Stitching code. You can see that there are lot of parameters you can change to make your code give some good stitching result. Also I would suggest using a small image (640 X480) for the feature detection step. Using small images is better than using very large images
How do I retrieve the rotation matrix, the translation vector and maybe some scaling factors of each camera using OpenCV when I have pictures of an object from the view of each of these cameras? For every picture I have the image coordinates of several feature points. Not all feature points are visible in all of the pictures.I want to map the computed 3D coordinates of the feature points of the object to a slightly different object to align the shape of the second object to the first object.
I was confronted with the same problem as you, in OpenCV. I had a stereo image pair and I wanted to computed the external parameters of the cameras and the world coordinates of all observed points. This problem has been treated here:
Assuming we have two cameras, where the first camera has external parameters RT = Matx::eye(). Now make a guess about the the rotation R of the second camera. For every pair of image points observed in both images, we compute the directions of their corresponding rays in world coordinates and store them in a 2d-array dirs (EDIT: The internal camera parameters are assumed to be known). We can do this since we assume that we know the orientation of every camera. Now we build an overdetermined linear system AC = 0 where C is the centre of the second camera. I provide you with the function to compute A:
I am using Field Maps for a data collection exercise to update the attributes for existing points, but the symbology for the points is not coming up on all of the devices, particularly the older ones. You can see the basemap imagery and if you click in an area where points exist, you will get the pop-up showing the attributes, but you cannot see the point itself.
I have an object moving along a conveyor line and am trying to use computer vision to track its position. Some of the objects that will pass through do not have "good features to track." However, template matching of the corners does seem to work.
If cv::goodFeaturesToTrack isn't giving me good points, am I out of luck? The corners seem like the distinguishing features, and I was hoping I could track them. Would template matching (possible taking rotation into account) be the best option to identify corners?
Remember that a routine like goodFeaturesToTrack() is based on image gradients, and gradients are implicitly tied to a single scale. Often an image region that has poor gradients at one scale has stronger gradients at a different scale. (Indeed the SIFT and SURF feature detectors mentioned by Alexander Reynolds above make use of filters at varying scales.)
Abstract:This paper presents a novel feature point descriptor for the multispectral image caseFar-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.Keywords: multispectral image descriptor; color and infrared images; feature point descriptor
The difficulty in finding correspondences between feature points from VS-LWIR images results from the nonlinear relationship between pixel intensities. Variations in LWIR intensities are related to variations in the temperature of the objects, while variations in VS intensities come from color object and light reflections. Therefore, this nonlinear relationship results in a lack of correlation between their respective gradients. Furthermore, LWIR images appear smoother, with loss of detail and texture [14], so that the detection of corners, as candidates for local descriptor points, is also poorly favored. In conclusion, most of the image processing tools that use gradient of pixels based descriptors need to be adapted, or otherwise they become useless. Figure 1 shows a couple of images, from the same scenario, obtained with a camera working in the visible spectrum and a camera in the infrared spectrum; their corresponding histograms are provided in the right column showing the lack of contrast in the infrared image as well as showing how some details are missed. In addition to the lack of contrast and missed details it can be observed that in the infrared image there is a big difference in the transformation of intensities with respect to the one presented in the visible spectrum. This fact becomes critical to define the method that best describes this kind of images, since the transformations of intensity pixels in the infrared spectrum are non linear or non correlated with respect to the corresponding visible ones. Additionally, it should be noticed that in the LWIR images other kinds of information is available, which is not present in the visible one. The latter is an important characteristic where there is not enough illumination in the given scene, for instance when driving a car at night (e.g., [6,15]).
The proposed scheme consists of a scale-space pyramid, like the one used by SIFT. Similarly, invariant features are used, but by modifying the feature vector in such a way to incorporate spatial information from the contours of each keypoint without using gradient information. This allows us to generate a correlated parameter space in both the VS and LWIR images. Our proposal uses a descriptor based on the edge histogram. This edge orientation histogram describes the shapes and contours from LWIR images, keeping in the scale-space their invariance. Figure 2 presents a flow chart of the proposed method. It consists of three steps, namely detection, description and matching, as detailed next.
Feature points, which will be also referred to as keypoints, are detected by using a scale-space pyramidal representation. They correspond to the maxima and minima of a difference of Gaussiass applied over a series of smoothed and resampled images [8]. The result from this first stage is a set of stable keypoints, similar to those resulting from classical SIFT. Note that this result is invariant to scale, position and orientation, which are needed for registration applications. In the current implementation potential keypoints are obtained by setting the SIFT like detector with the following parameters: sigma = 1.2 and threshold = 40. The same parameters' setting is used in both multispectral images resulting in a set of PVS and PLWIR keypoints. Each keypoint is denoted by a vector (xi, yi, σi), where (xi, yi) correspond to the location and (σi) is the scale of that pyramidal representation where the keypoint appears.
Detected feature points are described through the use of an Edge-Oriented-Histogram, which is the main contribution of current work and will be referred to henceforth as EOH. These EOHs incorporate spatial information from the contours in the neighbourhood of each feature point. They describe the shapes and contours from both VS and LWIR images. The idea behind the proposed descriptor is motivated by the nonlinear relationships between image intensities so that the descriptor should be mainly based on region information instead of pixel information. Hence, the proposed descriptor is based on the use of histograms of contours' orientations in the neighbourhood of the given keypoints. Initially, both images (VS and LWIR) are represented by means of their edges, which are extracted using the Canny edge detector algorithm [17]. In all the cases Canny's thresholds have been automatically set relative to the highest value of the gradient magnitude of the image and σ = 4. Once both images are represented by means of their edges, feature points detected in Section 2.1 are described as illustrated in Figure 3. The different steps of the feature point descriptor are detailed below.
The selection of the right window size (N) for every keypoint is an important factor of the proposed scheme. A window with a too small or big size will increase the number of wrong matching of keypoints. Table 1 presents the performance of the proposed approach when windows with different sizes are considered to compute the vector of characteristics mentioned above; these values represent the average obtained with the whole data set, which contains 100 pair of multispectral images. It can be seen that the best performance is obtained when windows with sizes in between 80 80 and 100 100 are used.
This data set is available through our website ( ) for evaluation and comparison with other multispectral feature point detectors and descriptors. The performance of the proposed approach is evaluated with a Precision and Recall scheme. Furthermore, results are compared with other implementations. Table 3 shows average results, computed over the whole data set, obtained with different descriptors; we can appreciate that the proposed approach has the best performance when compared with all the other methods.
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