Multi-target detection with rotations
We consider the multi-target detection problem of estimating a two-dimensional target image from a large noisy measurement image that contains many randomly rotated and translated copies of the target image. Motivated by single-particle cryo-electron microscopy, we focus on the low signal-to-noise regime, where it is difficult to estimate the locations and orientations of the target images in the measurement. Our approach uses autocorrelation analysis to estimate rotationally and translationally invariant features of the target image. We demonstrate that, regardless of the level of noise, our technique can be used to recover the target image when the measurement is sufficiently large.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Two-dimensional multi-target detection: an autocorrelation analysis approach
We consider the two-dimensional multi-target detection problem of recovering a target image from a noisy measurement that contains multiple copies of the image, each randomly rotated and translated. Motivated by the stru…
Vocal Bursts Valence PredictionGeneralized Non-orthogonal Joint Diagonalization with LU Decomposition and Successive Rotations
Non-orthogonal joint diagonalization (NJD) free of prewhitening has been widely studied in the context of blind source separation (BSS) and array signal processing, etc. However, NJD is used to retrieve the jointly diago…
blind source separationWhy Do Accumulated Transformations Extrapolate?
PaTH Attention showed that replacing RoPE's position-indexed rotations with accumulated data-dependent Householder reflections yields strong length extrapolation, though performance degrades at extreme context lengths. W…
Window-Object Relationship Guided Representation Learning for Generic Object Detections
In existing works that learn representation for object detection, the relationship between a candidate window and the ground truth bounding box of an object is simplified by thresholding their overlap. This paper shows i…
Objectobject-detectionObject DetectionRepresentation LearningCross-Domain 3D Equivariant Image Embeddings
Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations making them ideally suited to applications wh…
3D Shape ClassificationNovel View SynthesisObjectPose Estimation