matlab csc_registration

您好！关于Matlab中的csc_registration，它是用于图像配准（image registration）的函数。配准是将多幅图像中的对应点或特征对齐，使它们在空间上对应到同一位置，从而实现图像融合、图像处理等应用。

csc_registration函数实现了基于分段常数（piecewise constant）的图像配准方法。它将输入图像分成多个块，每个块中的像素灰度值近似为常数。然后，通过计算块之间的相似度，找到最佳的平移变换参数，从而实现图像的配准。

您可以在Matlab的文档中找到更多关于csc_registration函数的详细说明和使用方法。希望能对您有所帮助！如果您还有其他问题，请随时提问。

相关问题

csc_matrix包的源码

csc_matrix是SciPy中的一个稀疏矩阵格式，它使用压缩列存储格式（Compressed Sparse Column）来表示稀疏矩阵。下面是csc_matrix包的源码：

class csc_matrix(spmatrix):
    def __init__(self, arg1, shape=None, dtype=None, copy=False):
        _data_matrix.__init__(self)
        if isspmatrix_csc(arg1):
            if issubclass(arg1.__class__, self.__class__) and copy:
                arg1 = arg1.copy()
            self._set_self(arg1)
            return

        if dtype is None:
            dtype = getdtype(float, arg1)

        from .coo import coo_matrix
        if isspmatrix(arg1):
            if shape is None:
                shape = arg1.shape
            self._set_self(self.__class__((arg1.data, arg1.indices, arg1.indptr),
                                           shape=shape, dtype=dtype, copy=copy))
            return

        if shape is None:
            try:
                shape = arg1.shape
            except AttributeError:
                raise TypeError('expected dimension')

        if isintlike(arg1):
            n = int(arg1)
            self._shape = (n,n)
            self._check()
            return

        if isshape(arg1):
            self._shape = tuple(arg1)
            self._check()
            return

        # Now we must have something that we can convert to a csc_matrix
        # First convert to coo
        try:
            arg1 = coo_matrix(arg1, dtype=dtype).tocsc()
        except TypeError:
            raise ValueError("unrecognized format: {!r}".format(arg1))

        self._set_self(arg1)

    def _get_row_slice(self, i, cslice):
        if i < 0:
            M,N = self.shape
            i += M
        if cslice.step not in (1, None):
            raise ValueError('slicing with step != 1 not supported')
        start, stop = cslice.start, cslice.stop
        if start is None:
            start = 0
        if stop is None:
            stop = self.shape[1]
        if i < 0 or i >= self.shape[0]:
            raise IndexError('index out of bounds')
        if stop <= start:
            return array(self.dtype)
        indptr = self.indptr
        indices = self.indices
        startptr, stopptr = indptr[i], indptr[i+1]
        start_idx = searchsorted(indices[startptr:stopptr], start)
        stop_idx = searchsorted(indices[startptr:stopptr], stop)
        if indices[startptr+stop_idx-1] != stop:
            stop_idx = stop_idx - 1
        num_indices = stop_idx - start_idx
        if num_indices == 0:
            return array(self.dtype)
        idx_dtype = get_index_dtype((indices, indptr), maxval=max(self.shape))
        row_data = np.empty(num_indices, dtype=self.dtype)
        row_indices = np.empty(num_indices, dtype=idx_dtype)
        row_data[:] = self.data[startptr+start_idx: startptr+stop_idx]
        row_indices[:] = indices[startptr+start_idx:startptr+stop_idx]
        return csc_matrix((row_data, row_indices,
                           np.array([0, num_indices], dtype=idx_dtype)),
                          shape=(1, stop-start), dtype=self.dtype)

    def _get_col_slice(self, j, rslice):
        if j < 0:
            M,N = self.shape
            j += N
        if rslice.step not in (1, None):
            raise ValueError('slicing with step != 1 not supported')
        start, stop = rslice.start, rslice.stop
        if start is None:
            start = 0
        if stop is None:
            stop = self.shape[0]
        if j < 0 or j >= self.shape[1]:
            raise IndexError('index out of bounds')
        if stop <= start:
            return array(self.dtype)
        indptr = self.indptr
        indices = self.indices
        data = self.data
        i0 = searchsorted(indptr, j, side='left')
        i1 = searchsorted(indptr, j+1, side='left')
        idx_dtype = get_index_dtype((indices, indptr), maxval=self.shape[0])
        row_indices = np.empty(i1-i0, dtype=idx_dtype)
        row_data = np.empty(i1-i0, dtype=self.dtype)
        row_indices = indices[i0:i1]
        row_data = data[i0:i1]
        mask = (row_indices >= start) & (row_indices < stop)
        row_indices = row_indices[mask] - start
        return csc_matrix((row_data[mask], row_indices, np.array([0,len(row_indices)],
                                                                  dtype=idx_dtype)),
                          shape=(stop-start, 1), dtype=self.dtype)

    def _mul_scalar(self, other):
        return self.__class__((self.data * other, self.indices.copy(),
                                self.indptr.copy()), shape=self.shape,
                              dtype=self.dtype)

    def _mul_vector(self, other):
        M, N = self.shape
        if other.shape != (N,):
            raise ValueError("dimension mismatch")
        result = np.zeros(M, dtype=upcast_char(self.dtype.char,
                                                other.dtype.char))
        fn = getattr(_sparsetools, self.format + '_vec_mul')
        fn(M, N, self.indptr, self.indices, self.data, other, result)
        return result

    def _mul_multimatrix(self, other):
        M, K = self.shape
        _, N = other.shape
        result = spmatrix(dtype=self.dtype, shape=(M,N))

        o_data = other.data
        if isspmatrix(other):
            fn = getattr(_sparsetools, self.format + '_matmat_pass1')
            fn(M, K, N, self.indptr, self.indices,
               other.indptr, other.indices, o_data)
            fn = getattr(_sparsetools, self.format + '_matmat_pass2')
            fn(M, K, N, self.indptr, self.indices, self.data,
               other.indptr, other.indices, o_data, result.indptr,
               result.indices)
        else:
            fn = getattr(_sparsetools, self.format + '_matvec')
            for j in range(N):
                fn(M, K, self.indptr, self.indices, self.data, o_data[:,j],
                   result.data, j)

        result.sum_duplicates()
        return result

    def _get_dense(self, i, j):
        # Short-circuit zero case
        if self.nnz == 0:
            return np.zeros(self.shape, dtype=self.dtype)[i, j]

        M, N = self.shape
        if i < 0:
            i += M
        if j < 0:
            j += N
        if i < 0 or i >= M or j < 0 or j >= N:
            raise IndexError("index out of bounds")

        indptr = self.indptr
        indices = self.indices
        data = self.data

        i0 = indptr[j]
        i1 = indptr[j+1]

        if i0 == i1:
            return 0

        idx = searchsorted(indices[i0:i1], i) + i0
        if idx == i1 or indices[idx] != i:
            return 0

        return data[idx]

    def _get_sparse(self, i, j):
        from . import lil_matrix
        M, N = self.shape
        if i < 0:
            i += M
        if j < 0:
            j += N
        if i < 0 or i >= M or j < 0 or j >= N:
            raise IndexError("index out of bounds")
        indptr = self.indptr
        indices = self.indices
        i0 = indptr[j]
        i1 = indptr[j+1]
        data = self.data[i0:i1]
        indices = indices[i0:i1]
        indptr = np.array([0, len(data)], dtype=idx_dtype)
        return lil_matrix((data, indices, indptr), shape=(1, N))

    def __eq__(self, other):
        return self._eq_dense(other)

    def diagonal(self, k=0):
        if self.shape[0] != self.shape[1]:
            raise ValueError("diagonal is only defined for square matrices")
        if k > 0:
            n = self.shape[1] - k
            indptr = self.indptr[k:]
            indices = self.indices[indptr[0]:indptr[-1]]
            data = self.data[indptr[0]:indptr[-1]]
        else:
            n = self.shape[0] + k
            indptr = self.indptr[:n+1]
            indices = self.indices[indptr[0]:indptr[-1]]
            data = self.data[indptr[0]:indptr[-1]]
        return csc_matrix((data, indices, indptr), shape=(n,n))

    def sum(self, axis=None, dtype=None, out=None):
        if dtype is not None and not np.issubdtype(dtype, self.dtype):
            raise TypeError('Cannot upcast [%s] to [%s].' %
                            (self.dtype, dtype))
        if axis is None:
            return np.asarray(self.data.sum(dtype=dtype), dtype=dtype)
        elif axis == 0:
            if out is not None:
                raise ValueError("output array specified for reductions along axis 0,\
                                    but unsupported for csc_matrix")
            ret = np.empty(self.shape[1], dtype=dtype)
            for i in range(self.shape[1]):
                ret[i] = self.getcol(i).sum(dtype=dtype)
            return ret
        elif axis == 1:
            if out is not None:
                raise ValueError("output array specified for reductions along axis 1,\
                                    but unsupported for csc_matrix")
            ret = np.empty(self.shape[0], dtype=dtype)
            for i in range(self.shape[0]):
                ret[i] = self.getrow(i).sum(dtype=dtype)
            return ret
        else:
            raise ValueError("axis out of bounds")    

csc_matrix的实现主要基于COO格式，因为COO格式在行和列的切片操作上比较高效。除此之外，该源代码还实现了csc_matrix的加、减、乘、除、取负、转置、切片、求逆、求行列式、求特征值和特征向量等方法。

scipy.sparse.csc_matrix

scipy.sparse.csc_matrix是一个类，表示压缩稀疏列(CSC)矩阵。CSC矩阵是一种存储稀疏矩阵的方式，其中矩阵的每一列都被表示为一个单独的向量，这些向量存储在一个列表中，而每个非零元素的值和行索引则存储在另一个列表中。这种方式可以节省存储空间和计算时间，特别是在矩阵非常大且大部分元素为零的情况下。scipy.sparse.csc_matrix提供了许多方法，可以进行稀疏矩阵的操作，例如矩阵乘法、转置、切片和索引等。