diff --git a/camelot/image_processing.py b/camelot/image_processing.py
index 7b871011..1b5d962a 100644
--- a/camelot/image_processing.py
+++ b/camelot/image_processing.py
@@ -8,7 +8,6 @@
 
 def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):
     """Thresholds an image using OpenCV's adaptiveThreshold.
-
     Parameters
     ----------
     imagename : string
@@ -18,41 +17,29 @@ def adaptive_threshold(imagename, process_background=False, blocksize=15, c=-2):
     blocksize : int, optional (default: 15)
         Size of a pixel neighborhood that is used to calculate a
         threshold value for the pixel: 3, 5, 7, and so on.
-
         For more information, refer `OpenCV's adaptiveThreshold <https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold>`_.
     c : int, optional (default: -2)
         Constant subtracted from the mean or weighted mean.
         Normally, it is positive but may be zero or negative as well.
-
         For more information, refer `OpenCV's adaptiveThreshold <https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#adaptivethreshold>`_.
-
     Returns
     -------
     img : object
         numpy.ndarray representing the original image.
     threshold : object
         numpy.ndarray representing the thresholded image.
-
     """
     img = cv2.imread(imagename)
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-
-    if process_background:
-        threshold = cv2.adaptiveThreshold(
-            gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c
-        )
-    else:
-        threshold = cv2.adaptiveThreshold(
-            np.invert(gray),
-            255,
-            cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
-            cv2.THRESH_BINARY,
-            blocksize,
-            c,
-        )
+    if not process_background:
+        gray = np.invert(gray)
+    threshold = cv2.adaptiveThreshold(
+        gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, blocksize, c
+    )
     return img, threshold
 
 
+
 def find_lines(
     threshold, regions=None, direction="horizontal", line_scale=15, iterations=0
 ):
@@ -177,7 +164,6 @@ def find_contours(vertical, horizontal):
 
 def find_joints(contours, vertical, horizontal):
     """Finds joints/intersections present inside each table boundary.
-
     Parameters
     ----------
     contours : list
@@ -188,7 +174,6 @@ def find_joints(contours, vertical, horizontal):
         numpy.ndarray representing pixels where vertical lines lie.
     horizontal : object
         numpy.ndarray representing pixels where horizontal lines lie.
-
     Returns
     -------
     tables : dict
@@ -196,22 +181,12 @@ def find_joints(contours, vertical, horizontal):
         in that boundary as their value.
         Keys are of the form (x1, y1, x2, y2) where (x1, y1) -> lb
         and (x2, y2) -> rt in image coordinate space.
-
     """
-    joints = np.multiply(vertical, horizontal)
     tables = {}
     for c in contours:
         x, y, w, h = c
-        roi = joints[y : y + h, x : x + w]
-        try:
-            __, jc, __ = cv2.findContours(
-                roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE
-            )
-        except ValueError:
-            # for opencv backward compatibility
-            jc, __ = cv2.findContours(
-                roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE
-            )
+        roi = joints[y:y+h, x:x+w]
+        jc = cv2.findContours(roi.astype(np.uint8), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)[1]
         if len(jc) <= 4:  # remove contours with less than 4 joints
             continue
         joint_coords = []