G E N E R A L A P P R O A CH: LO O K I N G F O R D O M I N A N T O R I E N T A T I O N I N I M A G E P A T C H E S

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1 G E N E R A L A P P R O A CH: LO O K I N G F O R D O M I N A N T O R I E N T A T I O N I N I M A G E P A T C H E S In object categorization applications one of the main problems is that objects can appear in many different orientations. One way of coping with this could be to guess what is the orientation of the object, then rotate an image patch to a standard rotation and only use a single model to detect objects. In order to define that orientation image knowledge like structure and shape could be used. MATLAB: DEFINING DOMINANT ORIENTATION (SOURCE CODE: USB stick > TOBCAT > software > matlab > dominant_orientation.m) As a proof of concept, an algorithm based on image gradients, was created to define a dominant orientation of an image patch supplied. Basically several steps were followed to achieve the desired result. TEST DOMINANT GRADIENT DETECTION ON A CROSSOVER IN AERIAL IMAGERY For this the original image was put through some image processing algorithms: Based on the input image, gradients are calculated in X and Y direction and then combined into a single 2D gradient image. Based on this information it is then possible to define the orientation of the object, in this case a cross-over on the road.

2 Some point of attention: What to do on the edges? We do not want to create artefacts in the further processing. Possible solutions to tackle this can be o Using the closest edge value available. o Done by adding an extra argument to the smoothing filter. o smoothed = imfilter(imggs,kernel,'replicate','full'); After defining the gradient directions (visualized below) from the combined image gradient, we can look for a dominant gradient by binning the gradient in each image position. It is important to use the absolute sizes of the gradients, to be sure that edges that are countersigned, do not cancel each other. This is done by mapping the range of and degrees together. From that plot, a maximum is defined and used to give direction to a blue error, called the dominant gradient. Perpendicular to that we can find the dominant orientation, keeping in mind that the sign was ignored so it could be either way along the retrieved direction.

3 TESTS ON OTHER BUT SIMILAR INPUT DATA The algorithm was tested on other samples to see if the same result could be achieved.

4 TESTS ON OTHER OBJECT STRUCTURES, LIKE GRAIN OR IMPURITIES

5 TRANSLATION OF THE SOFTWARE TO A CPP IMPLEMENTATION (SOURCE CODE: USB stick > TOBCAT > software > cpp_windows > dominant_orientation.cpp) In order to be able to make an effective piece of software, the decision was made to translate this to a cpp implementation using the available OpenCV library. First results of a similar implementation: However a small mistake in the code resulted in a transformed image that was incorrectly. This was solved by redefining the relation between gradient and dominant orientation, which results in the result below: Applied on other image sets, the same result was achieved.

6 First set of conclusions: 1. The algorithm does its job if the object itself has a decent contrast compared to the background and if the object itself is defining the dominant orientation. 2. In case the dominant orientation is defined by clutter in the background, the algorithm fails miserably. This can however be countered by using smaller windows. 3. In the histogram binning, don t make bins to small, else the algorithm fails. (< 5 degrees) Tests on pedestrians were also promising, where the technique could be used to counter lens deformation.

7 If bin size is taken large enough, it even works decently with cluttered backgrounds like shown below. The reason of larger bins is to suppress the influence of small background clutter into the binning process.

8 ADAPTIVE WINDOW ROTATIONS IWT-TETRA TOBCAT (n ) In order to ensure that not too much image info is lost, an adaptive window rotation techniques was developed. Basically the image is first transposed into a buffered region then rotated to ensure no data except black pixels can be lost. LOOKING INTO ROTATION INVARIANCE BEHAVIOUR WITH OPENCV DETECTORS Finally this techniques was used to test, not taking into account detection times, if it was possible to use this technique to perform rotation invariant person detection. It was done using several frameworks. FIRST ATTEMPT, ROTATE THE IMAGE BY A DEGREE AND PERFORM LATENTSVM PERSON DETECTION

9 Basically the image was rotated by a single degree in steps and a detection was performed using the latentsvm approach imbedded in OpenCV. What we want to check is if the confidence score of a detection drops under changing rotation. The result was written to a file. Based on this information we conclude that the LatentSVM detector is -10 to +10 degrees rotation invariant if we set a certainty score threshold of 1. We apply the same technique for other detectors. SECOND ATTEMPT: APPLY REASONING ON HOG+SVM DETECTOR Here a large invariance is measured, but the computation time drops drastically also, which could be very interesting in several applications. However no confidence score is retrieved so we cannot define if the same certainty base is guaranteed.

10 APPLYING EVERYTHING TOGETHER IWT-TETRA TOBCAT (n ) Defining dominant gradient then do a rectification and rotation and applying a detection. This was compared in a GPU/CPU setup and with all angles or a single one. More of the above code samples can be found at USB stick > TOBCAT > software > cpp_windows > rotation_invariant_detection_hog.cpp USB stick > TOBCAT > software > cpp_windows > rotation_invariant_detection_hog_gpu.cpp USB stick > TOBCAT > software > cpp_windows > rotation_invariant_detection_latentsvm.cpp

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